COMPARING FUZZY LOGIC AND FUZZY C-MEANS (FCM) ON SUMMARIZING INDONESIAN LANGUAGE DOCUMENT.
Journal of Theoretical and Applied Information Technology
January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
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Journal of Theoretical and Applied Information Technology
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Lebanese International University,
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University Putra Malaysia,
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The University of Hong Kong, CHINA
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University Islamabad, PAKISTAN
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Journal of Theoretical and Applied Information Technology
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Dr. RIKTESH SRIVASTAVA
Assistant Professor, Information Systems
Skyline University College
P O Box 1797, Sharjah, UAE
Dr. BONNY BANERJEE
PhD in Computer Science and Engineering,
The Ohio State University, Columbus, OH, USA
Senior Scientist
Audigence, FL, USA
PROFESSOR NICKOLAS S. SAPIDIS
DME, University of Western Macedonia
Kozani GR-50100, GREECE.
Dr. NAZRI BIN MOHD NAWI
Software Engineering Department, Faculty of
Science Computer Information Technology,
Universiti Tun Hussein Onn
MALAYSIA
Dr. JOHN BABALOLA OLADOSU
Ladoke Akintola University of Technology,
Ogbomoso, NIGERIA
Dr. ABDELLAH IDRISSI
Department of Computer Science, Faculty of
Science, Mohammed V University - Agdal,
Rabat, MOROCCO
Dr. AMIT CHAUDHRY
University Institute of Engineering and
Technology, Panjab University, Sector-25,
Chandigarh, INDIA
Dr. ASHRAF IMAM
Aligarh Muslim University, Aligarh-INDIA
Dr. MOHAMMED ALI HUSSAIN
Dept. of Computer Science & Engineering, Sri
Sai Madhavi Institute of Science & Technology,
Mallampudi,
Rajahmundry, A.P, INDIA
Dr. KHALID HUSSAIN USMANI
Asst. Professor Department of Computer Science,
Arid Agriculture University,
Rawalpindi, PAKISTAN
Dr. GUFRAN AHAMD ANSARI
Qassim University, College of Computer Science,
Ministry of Higher Education,
Qassim University, KINGDOM OF SAUDI
ARABIA
Dr. Defa Hu
School of Information, Hunan University of
Commerce
Changsha 410205, Hunan, P. R. of China
Elite Panel Members Have A Decision Weight Equivalent of Two Referees (Internal OR External).
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Acceptance/Rejection And Their Opinion Is Considered As Final.
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Journal of Theoretical and Applied Information Technology
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Journal of Theoretical and Applied Information Technology
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ISSN: 1992-8645
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E-ISSN: 1817-3195
COMPARING FUZZY LOGIC AND FUZZY C-MEANS (FCM)
ON SUMMARIZING INDONESIAN LANGUAGE DOCUMENT
1
DWI ADE RIANDAYANI, 2I KETUT GEDE DARMA PUTRA, 3PUTU WIRA BUANA
123
Department of Information Technology, Udayana University, Indonesia
E-mail: [email protected], [email protected], [email protected]
ABSTRACT
Text summarization is one way to get the information which is quite effective. Approach techniques of the
text summarization are classified into two, namely abstractive and extractive. This paper is focused on the
extractive techniques so that the output of the application is important sentences which are taken from the
original text intact without modification verbally. There are four majors processes in this research namely
preprocessing stage, scoring features, optimization of summarization by two methods (Fuzzy Logic and
Fuzzy C-Means), and extraction of summary results. This research use 7 features to calculate the score of
each sentence. The core of this research is to compare reliability Fuzzy logic and Fuzzy C-Means method in
optimizing process of summary results. The results showed that the method of Fuzzy Logic is
outperformed Fuzzy C-Means method with the closest similarity ratio to summarize manually by humans
with percentage accuracy of Fuzzy Logic is 58.25% while Fuzzy C-Means method is 54.33%. Increased
accuracy occurs when there is additional compression rate in the amount of 1.67% for Fuzzy Logic Method
and 4.5% for Fuzzy C-Means.
Keywords: Summarization, Text Features, Scores Sentences, Fuzzy Logic, Fuzzy C–Means
1.
INTRODUCTION
Text Summarization is very important and
useful technique to identify the topic of a text
quickly and accurately[1]. Summarization is the
process of condensing a source text into a shorter
version preserving its informationcontent[2]. The
goal is to help prospective readers save time and
effort in finding useful information in a text.
In a research, automatic text summarization
uses variety of methods and approaches[1]. The
previous research use the genetic algorithm as a
method to select the best sentence of text and use a
variety of combination feature amount. The result is
percentage of accuracy in each feature combination
[3]. Another research use Pseudo Genetic And
Probabilistic method for text summarization [4].
Techniques of text summarization can be
classified into two categories: extractive and
abstractive. Development of automatic text
summarization can apply one of the method or both
at once. Extractive summarization is technique of
sentence selection that represents the topic of a text
and incorporates it into the new text which is
shorter than the original, without modified from the
original text, while abstractive method produces a
summary with phrases that may not be found in the
original text [4]. This research use 3 kind of
summarizing: deductive, inductive and ineratif.
Deductive is a kind of finding mind topic in first
sentence of each paragraph. So the first sentence
have a biggest score among the following text
whole the paragraph. While the topic where located
in the end of each paragraph is named inductive and
topic where located in the centre of paragraph
named ineratif.
Process of summarizing includes four stages,
as follows: 1. text preprocessing (phase separation
process includes the sentence, the case folding, the
process of filtering the sentence, the word
tokenizing and stemming), 2. Feature Scoring, 3.
Sentence Selection (this stage optimized by using
Fuzzy Logic and Fuzzy C-Means), 4. Process of
preparing selected sentences. The research in this
thesis is more focused on a comparison of the
results of summarizing using the Fuzzy Logic and
Fuzzy C–Means thus, it will proof which method
will give better results of summarizing.
Fuzzy Logic is a knowledge representation
that is constructed by if-then rules. One key feature
of fuzzy rule based systems is their
comprehensibility because each fuzzy rule is
linguistically interpretable [5]. Characteristics of
Fuzzy Logic method are: 1. problem solving is
done by describing the system not by numbers, but
linguistically,
or
variables
that
contain
718
Journal of Theoretical and Applied Information Technology
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© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
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uncertainty/ambiguity , 2. Use of if-then rules to
clarify the relationship between one variable with
another, 3. Describe the system using fuzzy
algorithm [6]. While Fuzzy C-Means is a technique
of data clustering where the existence of each data
point in a cluster is determined by the degree of
membership.
This technique was first introduced by Jim
Bezdek in 1981 which then continues to develop
[7]. At first basic concept of FCM is to determine
cluster centers that will mark the location of the
average for each cluster. In the initial condition, the
cluster center is still not accurate. Each data point
has a degree of affiliation for each cluster. By
improving the cluster center and the degree of
affiliation of each data point repeatedly thus, it will
be seen that the cluster center will move towards
the right location. The Fuzzy C-Means clustering
algorithm is based on the minimization of an
objective function called C-means functional [8].
Both of these methods will be the focus of the test
in this paper.
2.
SUMMARIZATION APPROACH
2.1 Data Set and Preprocessing
Preprocessing is early stages to prepare
summarizing input text into data that is ready to be
processed. Stage that is contained in the text
preprocessing are segmentation paragraph, sentence
segmentation, folding case, sentence filtering,
tokenizing and stemming.
Paragraphs Segmentation is a limitation and
separation of text into paragraphs. Sentence
Segmentation is the separation paragraphs into
sentences. Characters besides the 'a- z' characters
are removed from the text and capital letters
converted to lowercase, this process is called case
folding. Each sentences have to be filtered from
stop word towards the text content. Tokenizing is
the stage to separate the sentence into its
constituent words using the character "white space"
as delimiter. In the end, the results of tokenizing
processes will be processed to the stage again to
stemming phase to get the base word of each word
[9] [10].
2.2 Selected Feature
After preprocessing, each sentence in the text
is represented by weight which is obtained from
scoring towards several features. Features are
carefully selected so that the system is able to
deliver approximately correct results. This paper
focus on seven features for each sentence. Each
feature will be worth between '0' and '1' [9].
E-ISSN: 1817-3195
2.2.1
Sentence Position (F1)
Sentence position can determine whether the
sentence is important or not in representing the
content of text. In general, the first sentence in each
paragraph has the highest score is called the
deductive types of summarizing. But it is possible
that the main topic of the text is exist at the end of
paragraph it is named inductive summarizing.
Paragraph that the main topic is in the end of
paragraph named ineratif summarizing. In this
study, we use the three types of summarizing.
Value of the feature position is determined from the
results of division of this sentence position (X) on
each paragraph by the number of sentences of each
paragraph which is contained in the paragraphs (N).
Sentence Position can be calculated as Eq. 1.
(1)
2.2.2
Sentence Length Feature (F2)
Sentence length feature is used for filtering short
sentences that sometimes only contain names or
other authors. This short sentence is not expected to
be part of the summary. Calculation of the value of
this feature is the result of length sentences divided
with the number of words in a sentence (X) and the
number of the entire text (S).
(2)
2.2.3
Title feature (F3)
The words in the sentence that contain the title has
a high score. This stage calculates the suitability of
the words with content in the title. The title is
generally made up of several words, then the text of
the title text needs to undergo preprocessing. Then
token in the text checked against token title. Title
feature can be calculated as Eq. 3. Eq. 3.
(3)
2.2.4
Term Weight (F4)
Number of occurrences of the term in a sentence
can be used for detecting important phrase. Score
each sentence can be determined by summing the
scores of each constituent word. The term weight
Method calculates weights sentence so that this
method is referred as tf.isf (Term Frequency.
Inverse Sentence Frequency). Weight of each
sentence in the text is obtained by the
multiplication of the number of occurrences of the i
term in the text with its inverse value, this method
can be calculated as Eq. 4.
719
(4)
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The next score for each sentence is calculated by
dividing the sum of all the scores contained in I
sentence to score maximum sentence on
mathematical text. The calculation can be seen in
equation 5.
E-ISSN: 1817-3195
these methods, the following stages of the system is
shown in Figure 1.
(5)
2.2.5
Entity Word or term (F5)
Frequency of occurrence of the term or entity that
occurs in the source text can be become a factor to
calculate importance words. The word entity can be
a one's name, job title, name of the country and so
forth. Scores on this feature can be calculated from
the ratio of the number of entities/terms that appear
in the sentence to the sentence length.
(6)
2.2.6
Numerical Data (F6)
Sentences containing numerical data are assumed to
be the word that is quite important and most likely
have to be included into the results summary. Score
of features numerical data were calculated as the
ratio of the number of numerical data contained in
the sentence to sentence length.
(7)
2.2.7
Thematic Word (F7)
Thematic word is a word that appears quite
frequently in a text. This feature is important
because the words which contain thematic word can
be indicated as important sentences. The 10 words
with the highest frequency is used as thematic word
in this feature. Score calculation of thematic word
feature is the quotient between the number of words
in sentences with thematic maximum value of the
thematic word whole the sentence.
(8)
3.
Figure 1: System Step
From Figure 1, the results of the optimization phase
using Fuzzy Logic and Fuzzy C-Means is done
after preprocessing stage and scoring feature.
Success of automatic text summarizing is the
selection of sentence at this stage of the
optimization results [9]. Optimization phase will
produce sentences with high scores as a result of a
summary.
3.1 Optimizing with Fuzzy Logic
Shown earlier in Figure 1, that there are 3
processes in stages of Optimization of Fuzzy
Logic: Fuzzifier, Inference Engine and Defuzzifier.
Input on fuzzy logic method is score 7 feature of all
sentences. Enter the crisp fuzzification stage that
will be supposed to be into some fuzzy sets namely:
very low (SR), low (L), medium (M), high (H) and
very high (ST). Membership functions which are
used in here is "Curve Triangle" with a membership
limit shown in Table 1.
THE METHODOLOGHY
In this section explain how to extract the
summary result that will be optimized by using
Fuzzy Logic and Fuzzy C-Means method. As
explained in the Introduction, the core of this paper
is to compare the reliability of the two methods that
use in this research. Before describing more about
720
Table 1: Boundary Value of Fuzzy Logic Membership
SR
R
M
T
ST
a
0.1
b
0
c
0.2
0.3
0.6
0.8
0.25
0.5
0.75
1
0.4
0.7
0.9
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Rules of Fuzzy Logic membership functions shown
in Eq. 9 and the affiliation function graph which is
shown in Figure 2.
(9)
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word (x5), Numerical Data (x6), Thematic Words
(x7).
FCM output is not fuzzy inference system, but
a row of cluster centers and several degree of
affiliation for each data point. This information can
be used to construct fuzzy inference system. Fuzzy
C-Means algorithm is as follows:
Step 1: Input data that will be clustered, X , in the
form of matrix n x m (n = number of data samples ,
m = attributes of each data = 70, where Xij = data
sample i (i = 1,2,…, n), attribute j (j = 1,2,…,7)
Step 2: Set the number of clusters = c = 3, Rank =
w = 2, Maximum iterations = 100, Error smallest
expected = 0.00001, initial objective function (P0)
= 0, the initial iteration (t) = 1.
Figure 2:Graph of Fuzzy Logic Membership
Degree of fuzzy logic affiliation are in the
range 0-1. Later, each sentence in the text will be
measured by degrees 7 features that have been
described previously.
In many studies, antecedent fuzzy sets
were generated and tuned by numerical input data
for rule base construction to improve the
classification accuracy of FRBCSs [11] [12]. The
most important part of inference engine is fuzzy IFThen rules [9]. The experience of the human
controller is usually expressed as some linguistic
"IF-THEN" rules that state in what situation(s)
which action(s) should be taken [13]. One example
of IF-THEN rule is as follows: IF (Position
sentence is Very High) and (Long sentence is Low)
and (Title Feature is High) and (Term Weight is
Medium) and (Noun Entities is Low) and ( Data
Numerical is Very High ) and ( Thematic Word is
Very High) THEN sentence is important.
Tsukamoto is a settlement that will be used in the
Inference Engine [14]. In the end of the stage,
defuzzifier crisp sentences are classified into
categories important, average and not important,
then the next set is converted into crisp numbers of
sentence weight.
3.2 Optimizing with Fuzzy C-Means
Optimization with Fuzzy C-Means method
will classify the input sentence into three clusters
include: cluster of important sentences, sentence of
the average cluster and cluster sentences are not
important. The input which is used in the form of
sentences and sentence attributes in form of
features. There are 7 Attributes that are used
namely: Position Sentence (x1), length of sentence
(x2), Title Feature (x3), Term Weight (x4), Entities
Step 3: Generate random numbers µik, i = 1,2,…,n;
k = 1,2,3; as elements initial partition matrix U.
Count the number of each column:
(10)
with i = 1,2,…,n
Calculate:
(11)
Step 4: Calculate cluster k: Vkj, with k = 1,2,3; and
j = 1,2,…,m.
(12)
Step 5: Calculate the objective function at iteration
t, Pt
(13)
Step 6: Calculate the change in the partition matrix
(14)
With: i = 1,2,…,n; and k = 1,2,…,m
721
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
Step 7: Check the condition to stop. If the value of
(|Pt – Pt-1| < 0.00001) or (t > 100) then stop and
then specify the cluster affiliation. If not: t = t + 1,
repeat step 4.
E-ISSN: 1817-3195
Combination of recall and precision values then
generate f -measure.
(17)
3.3 Sentence Extraction
Extraction of Fuzzy Logic and Fuzzy CMeans will generate sentences with high scores in
descending order as a result of a summary.
Sentences were extracted and taken as much as
30% and 25% the text of the original text.
Possibilities of extraction is designed varied to
determine the composition of the best summaries
results of Fuzzy Logic and Fuzzy C-Means were
compared with the ideal of summarization by
humans. The sentence will be re-ordered according
to the actual order of the original text. In addition,
this study will also compare the results of
summarization is deductive, inductive, ineratif by
using a composition of the best extraction results
summaries. This study examined a sample of 20
Indonesian language text obtained from several
news sites are: kompas.com, kapanlagi.com and
yahoo.co.id.
4.
Table 2 shows the sample text that will be
processed by our system.
Table 2: Sample of Indonesian Text
Memaknai Kemerdekaan Di Hari Kemenangan
Jakarta - Ya, 68 tahun sudah Indonesia
merdeka. Sebagai Negara maritim dan kepulauan
terbesar di dunia, Indonesia juga memiliki ragam
etnis dan budaya terbanyak dibandingkan Negara
lain. Suatu kerukunan dalam keberagaman yang patut
dibanggakan sekaligus pencapaian yang tak mudah
dilakukan. Tentu, sepanjang perjalanan itu, banyak
kemajuan yang sudah dicapai, meski juga
menyisakan keberharapan. Salah satu yang perlu kita
renungkan adalah kesejahteraan bagi seluruh rakyat
Indonesia.
Jika kita merenungkan kembali cita-cita
bangsa yang dituangkan dalam pembukaan UndangUndang Dasar 1945, terlihat bahwa Indonesia adalah
bangsa yang besar. Dan oleh karenanya,
kemerdekaan harus diwujudkan untuk melindungi
segenap bangsa, memajukan kesejahteraan umum,
mencerdaskan kehidupan bangsa, sekaligus ikut
melaksanakan ketertiban dunia. Sebuah cita-cita
luhur yang diwariskan kepada kita untuk
mencapainya, bahkan di kala kondisi perekonomian
dunia sedang lesu.
Meski banyak lembaga ekonomi dunia
memuji pertumbuhan ekonomi Indonesia, namun
banyak hal membutuhkan pembenahan guna lebih
menguatkan ekonomi nasional. Sebagaimana yang
kita alami, harga berbagai kebutuhan pokok sudah
merangkak naik sejak awal Ramadhan, mencapai
puncaknya di hari Kemenangan. Atas berbagai
kenaikan harga ini, banyak saudara kita yang
merasakan ketidaknyamanan bahkan kesulitan dalam
menjalani hidupnya.
Satu hal yang pasti, Negara ini masih
membutuhkan pembenahan di berbagai bidang. Tentu
saja, seluruh masyarakat harus terlibat dalam
pembenahan, sekaligus memperkuat nasionalisme
yang ada dalam konsensus bersama: UUD 1945,
Pancasila, Negara Kesatuan Republik Indonesia, serta
Bhinneka Tunggal Ika. Yang harus kita lakukan
adalah menjalani peran masing-masing dalam
kehidupan bernegara: giat bekerja untuk membangun
bangsa, aktif mengawasi jalannya pemerintahan serta
memenuhi kewajiban perpajakan.
RESULT AND ANALYSIS
The main objective of this study was to
compare the reliability of the method of Fuzzy
Logic and Fuzzy C-Means in terms of optimization
of the results of summarization text in Indonesian
language. Therefore, we obtain two results, one for
summarization method (deductive, inductive,
inertif) and the other for extraction composition.
Research has also been done by summarizing the
20 samples Indonesian language text that comes
from multiple news sites.
Outcome evaluation system using the formula
precision, recall and f-measure which is called
intrinsic method [15]. Evaluation creates an ideal
set of summaries, one for each text to test the text
then comparing the results with a summary of the
ideal system summary. Measurement is in overlap
of content, it is often referred to recall and precision
sentence or phrase, but sometimes with a single
word overlap.
(15)
(16)
The content of Table 2 separated into each
sentence. White space character use as a delimiter
to separated text into sentences. The stop word is
also removed from sentence. Sentence then through
722
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
Table 5:Result of Comparison Compression Rate
Average F-measure
Optimizing
Method
30%
25%
Fuzzy Logic
0.47632
0.47572
Fuzzy C-Means
0.46759
0.45592
the filtering stage and thus case folding. Table 3
show the result.
Table 3: Separated Text into Sentences
No.
Sentences
Sentence
1.
jakarta 68 indonesia merdeka
2.
maritim kepulauan indonesia ragam etnis
budaya dibandingkan
3.
kerukunan
keberagaman
patut
dibanggakan pencapaian
4.
perjalanan kemajuan dicapai menyisakan
keberharapan
5.
renungkan kesejahteraan Indonesia
6.
merenungkan
cita
cita
bangsa
dituangkan pembukaan undang undang
1945 indonesia bangsa
7.
kemerdekaan diwujudkan melindungi
segenap
bangsa
memajukan
kesejahteraan mencerdaskan bangsa
melaksanakan ketertiban
8.
kemerdekaan diwujudkan melindungi
segenap
bangsa
memajukan
kesejahteraan mencerdaskan bangsa
melaksanakan ketertiban
9.
ekonomi memuji ekonomi indonesia
membutuhkan pembenahan menguatkan
ekonomi
10.
alami pokok merangkak ramadhan
puncaknya kemenangan
11.
saudara merasakan ketidaknyamanan
kesulitan hidupnya
12.
membutuhkan pembenahan
13.
terlibat
pembenahan
memperkuat
nasionalisme konsensus
uud 1945
pancasila kesatuan indonesia bhinneka
tunggal ika
14.
lakukan peran bernegara
giat
membangun bangsa aktif mengawasi
jalannya
memenuhi
kewajiban
perpajakan
E-ISSN: 1817-3195
Table 6 shows the f-measure Fuzzy Logic
outperformed Fuzzy-C Means and following by the
graphic show in figure 3. This result are calculated
with 30% compression rate.
Table 6:Result of Comparison F-Measure
Teks
Precission
Recall
F-measure
Accuration
1
0.2000
0.6667
0.3077
33.33%
2
0.0000
0.0000
0.0000
0
3
0.5000
0.6000
0.5455
60%
4
0.6000
0.7500
0.6667
75%
5
0.4000
0.6667
0.5000
33.33%
6
0.2500
0.3333
0.2857
33.33%
7
0.2000
0.3333
0.2500
33.33%
8
0.1429
0.2500
0.1818
75%
9
0.6667
1.0000
0.8000
100%
10
0.7500
1.0000
0.8571
100%
11
0.5000
1.0000
0.6667
100%
12
0.4000
0.5000
0.4444
50%
13
0.5000
1.0000
0.6667
100%
14
0.6667
1.0000
0.8000
50%
15
0.2500
0.5000
0.3333
50%
16
0.5000
0.6667
0.5714
67%
17
0.0000
0.0000
0.0000
0%
18
0.7500
1.0000
0.8571
100%
19
0.3333
0.5000
0.4000
50%
20
0.0000
0.0000
0.0000
0%
Average Accuration :
58.83%
The result of stemming for the sample shown on
Table 4 below.
Table 4: Stemming Result
No.
No.
Token
Token
Sentence
1
1
jakarta
2
1
68
3
1
indonesia
4
1
merdeka
5
2
maritime
1.0000
F-Measure
0.8000
Table 5 shows the f-measure comparison results
between the method of Fuzzy Logic and Fuzzy CMeans with the composition of the extraction of 40
%, 30 %, 25 % and 20 %.
723
0.6000
Fuzzy
Logic
0.4000
0.2000
0.0000
1 3 5 7 9 11 13 15 17 19
Fuzzy
CMeans
Figure 3:Graph of Comparison F-measure
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
5.
www.jatit.org
CONCLUSION
This paper have compared Fuzzy Logic and
Fuzzy C-Means as a method to optimize the
extraction of sentences in the automatic text
summarization. The research selected important
sentences based on 7 features: sentence position,
sentence length, feature title, term weight, entity
word or term, numerical data and thematic word.
The deductive type is use on summarization and
then this research applied to two sentences
compression rate was equal to: 30% and 25%. It
turned out the best combination on the compression
rate of the summary which is obtained by 30%.
From research obtained accuracy of 58.25% for the
Fuzzy Logic method and 55.5 % for Fuzzy CMeans method. Acuracy increase occur when there
is additional compression rate in the amount of
1.67% for Fuzzy Logic Method and 4.5% for Fuzzy
C-Means. Fuzzy Logic method was able to
optimize the results of summarizing text better than
the Fuzzy C-Means method in automatic text
summarization.
[9]
[10]
[11]
[12]
[13]
[14]
REFRENCES :
[1] H. P. Luhn, "The automatic creation of
literature abstracts," IBM J. Res. Dev., vol. 2,
pp. 159-165, 1958.
[2] Regina Barzilay, Michael Elhadad, "Using
lexical chains for text summarization,"
Proceedings of Intelligent Scalable Text
Summarization Workshop, 1997.
[3] Aristoteles.
"Pembobotan
Fitur
Pada
Peringkasan
Teks
Bahasa
Indonesia
Menggunakan Algoritma Genetika." Thesis,
Pertanian Bogor Institute, 2011.
[4] Albaraa Abuobieda M. Ali, et all., "Pseudo
Genetic And Probabilistic-Based Feature
Selection Method For Extractive Single
Document Summarization," JATIT, Vol. 32
No.1, 2011.
[5] O. Dehzangi Et. all., "Efficient fuzzy rule
generation: A new approach using data mining
principles and rule weighting," IEEE FSKD,
2007.
[6] Anto
Satriyo
Nugroho,
“Pengantar
Softcomputing,”
2003.
Available
at:
http://asnugroho.net/papers/ikcsc.pdf
[7] James C. Bezdek et all., "FCM: The Fuzzy
C-Means Clustering Algorithm," Computers
& Geosciences, Vol. 10, No. 2-3, pp. 191-203,
1984.
[8] Neha Jain and Seema Shukla, "Fuzzy
Database Using Extend Fuzzy C-Means
[15]
724
E-ISSN: 1817-3195
Clustering," IJERA. Vol. 2, Issue 3, May-Jun
2012, pp.1444-1451.
Ladda Suanmali, et all., "Fuzzy Logic Based
Method for Improving Text Summarization,"
IJCSIS, Vol. 2, No. 1, 2009.
Talla, fadillah Z. "A Study of Stemming
Effects on Information Retrieval in Bahasa
Indonesia," Master of Logic Project. Institute
for Logic, Language and Computation.
Universiteit
van
Amsterdam.
The
Neatherlands.
D. Nauck and R. Kruse, “A neuro-fuzzy
method to learn fuzzy classification rules from
data,” Fuzzy Sets and Systems, pp. 277-288,
1997.
S. Abe and R. Thawonmas, “A fuzzy classifier
with ellipsoidal regions,” IEEE Trans. on
Fuzzy Systems, pp. 358-368, 1997.
Li-Xin Wang and M. Mendel, "Generating
Fuzzy Rules by Learning from Examples,"
IEEE Transaction on Systems, Man, and
Cybernetics, Vol.22, No. 6, 1992.
Kamble P.N, “FLC Modeling of Classical
EEG Signals Model by the Technique
Tsukamoto Fuzzy Rule Base,” International
Journal of Statistika and Mathematika.
Volume 7, Issue 3, 2013 pp 52-57.
Martin Hassel. “Evaluation of Automatic Text
Summarization,”
Stockholm.
Licentiate
Thesis. 2004.
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Subject Area: Computer Science | Mathematics
Subject Category: Computer Science (miscellaneous)
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, Theoretical Computer Science
Publisher: Asian Research Publishing Network (ARPN). Publication type: Journals. ISSN: 18173195, 19928645
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2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Data
2011
2012
0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,137 0,167
Total Documents
0
0
0
0
0
0
0
0
0
0
0
222
229
622
Total Docs. (3years)
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0
0
0
0
0
0
0
0
0
222
451
Total References
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61
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4
17
Citable Docs. (3years)
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0
0
0
0
0
0
0
0
0
0
0
222
451
Cites / Doc. (4years)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,27
0,39
Cites / Doc. (3years)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,27
0,39
Cites / Doc. (2years)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,27
0,39
3.880 4.120 9.475
178
References / Doc.
0,00 0,00 0,00 0,00 0,00 0,00
0,00 0,00
0,00
0,00
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15,23src="http://ww
January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
E-ISSN: 1817-3195
JOURNAL OF THEORETICAL AND APPLIED
INFORMATION TECHNOLOGY
EDITORIAL COMMITTEE
NIAZ AHMAD
(Chief Editor)
Professor, FCE, MOE, H-9 Islamabad
PAKISTAN
SHAHBAZ GHAYYUR
(Co- Chief Editor)
Assistant Professor, DCS, FBAS, International Islamic University Islamabad,
PAKISTAN
SAEED ULLAH
(Associate Editor)
Assistant Professor, DCS, Federal Urdu University of Arts, Science & Technology Islamabad,
PAKSITAN
MADIHA AZEEM
(Associate Editor)
Journal of Theoretical and Applied Information Technology, Islamabad.
PAKISTAN
SALEHA SAMAR
(Managing Editor)
Journal of Theoretical and Applied Information Technology, Islamabad.
PAKISTAN
KAREEM ULLAH
(Managing Editor)
Journal of Theoretical and Applied Information Technology, Islamabad.
PAKISTAN
SHAHZAD A. KHAN
Lecturer IMCB, FDE Islamabad, PAKISTAN
(Managing Editor/Linguists & In-charge Publishing)
Journal of Theoretical and Applied Information Technology, Islamabad.
PAKISTAN
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JOURNAL OF THEORETICAL AND APPLIED
INFORMATION TECHNOLOGY
REGIONAL ADVISORY PANEL
Dr. SIKANDAR HAYAT KHIYAL
Professor &Chairman DCS& DSE, Fatima Jinnah Women University, Rawalpindi, PAKISTAN
Dr. MUHAMMAD SHER
Professor &Chairman DCS, FBAS, International Islamic University Islamabad, PAKISTAN
Dr. ABDUL AZIZ
Professor of Computer Science, University of Central Punjab, PAKISTAN
Dr. M. UMER KHAN
Asst. Professor Department of Mechatronics, Air University Islamabad, PAKISTAN
Dr. KHALID HUSSAIN USMANI
Asst. Professor Department of Computer Science, Arid Agriculture University,
Rawalpindi, PAKISTAN
ii
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JOURNAL OF THEORETICAL AND APPLIED
INFORMATION TECHNOLOGY
Dr. CHRISTEL BAIER
Technical University Dresden, GERMANY
Dr KHAIRUDDIN BIN OMAR
UniversitiKebangsaanMalysia, 43600 Bangi
Selangor Darul-Ehsan, MALYSIA
Dr. YUSUF PISAN
University of Technology, Sydney, AUSTRALIA
Dr. S. KARTHIKEYAN
Department of Electronics and Computer
Engineering, Caledonian College of Engineering,
OMAN (University College with Glascow
University, Scotland, UK)
DR. YUXIN MAO
School Of Computer & Information Engineering
Zhejiang Gongshang University, CHINA
Dr. ZARINA SHUKUR
FakultiTeknologidanSainsMaklumat, University
Kebangsaan MALYSIA
Dr. NOR AZAN MAT ZIN
Faculty of Information Science & Technology,
National University of MALYSIA
Dr. R.PONALAGUSAMY
National Institute of Technology, Tiruchirappalli,
Tamil Nadu, INDIA
Dr. PRABHAT K. MAHANTI
University of New Brunswick, Saint John, New
Brunswick, CANADA
Dr. NITIN UPADHYAY
Birla Institute of Technology and Science (BITS),
Pilani-Goa Campus, INDIA
Dr. S.S.RIAZ AHAMED
Mohamed Sathak Engineering College, Kilakarai,
&Sathak Institute of Technology,
Ramanathapuram , Tamilnadu, INDIA
Dr. A. SERMET ANAGÜN
Eskisehir Osmangazi University, Industrial
Engineering Department, Bademlik Campus,
26030 Eskisehir, TURKEY.
Dr. YACINE LAFIFI
Department of Computer Science, University of
Guelma, BP 401, Guelma 24000, ALGERIA.
Dr. CHRISTOS GRECOS
School Of Computing, Engineering And Physical
Sciences University Of Central Lancashire.
UNITED KINGDOM
Dr. JAYANTHI RANJAN
Institute of Management Technology
Raj Nagar, Ghaziabad, Uttar Pradesh, INDIA
Dr. ADEL M. ALIMI
National Engineering School of Sfax (ENIS),
University of SFAX, TUNISIA
Dr. RAKESH DUBE
Professor & Head, RKG Institute of Technology,
Ghaziabad, UP, INDIA
Dr. ADEL MERABET
Department of Electrical & Computer
Engineering, Dalhousie University, Halifax,
CANADA
Dr. HEMRAJ SAINI
CE&IT Department, Higher Institute of
Electronics, BaniWalid. LIBYA
Dr. MAUMITA BHATTACHARYA
SOBIT, Charles Sturt University
Albury - 2640, NSW, AUSTRALIA
Dr. MOHAMMAD TENGKU SEMBOK
Universiti Kebangsaan MALYSIA
iii
Journal of Theoretical and Applied Information Technology
January 2014. Vol. 59 No.3
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ISSN: 1992-8645
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E-ISSN: 1817-3195
Dr. SEIFEDINE KADRY
Lebanese International University,
LEBONON
Dr. AIJUAN DONG
Department of Computer Science
Hood College Frederick, MD 21701. USA
Dr. ZURIATI AHMAD ZUKARNAIN
University Putra Malaysia,
MALAYSIA
Dr. HEMRAJ SAINI
Higher Institute of Electronic, BaniWalid
LIBYA
Dr. CHELLALI BENACHAIBA
University of Bechar, ALGERIA
Dr. MOHD NAZRI ISMAIL
University of Kuala Lumpur (UniKL) MALYSIA
Dr. VITUS SAI WA LAM
The University of Hong Kong, CHINA
Dr. WITCHA CHIMPHLEE
SuanDusitRajabhat University, Bangkok,
THAILAND
Dr. S. KARTHIKEYAN
Caledonian College of Engineering,
OMAN
Dr. SIDDHIVINAYAK KULKARNI
University of Ballarat, Ballarat,
AUSTRALIA
Dr. E. SREENIVASA REDDY
Principal - VasireddyVenkatadri Institute of
Technology, Guntur, A.P., INDIA
Dr. DRAGAN R. MILIVOJEVIĆ
Mining and Metallurgy Institute BorZelenibulevar
35, 19210 Bor, SERBIA
Dr OUSMANE THIARE
Gaston Berger University, Department of
Computer Science, UFR S.A.T, BP 234 SaintLouis SENEGAL
Dr. SANTOSH DHONDOPANT KHAMITKAR
RamanandTeerthMarathwada University, Nanded.
Maharashtra 431605, INDIA
Dr. M. IQBAL SARIPAN
(MIEEE, MInstP, Member IAENG, GradBEM)
Dept. of Computer and Communication Systems
Engineering, Faculty of Engineering, Universiti
Putra MALAYSIA
Dr. E. SREENIVASA REDDY
Principal - VasireddyVenkatadri Institute of
Technology, Guntur, A.P., INDIA
Dr. T.C.MANJUNATH,
Professor & Head of the Dept.,
Electronicis& Communication Engg. Dept,
New Horizon College of Engg.,
Bangalore-560087, Karnataka, INDIA.
Dr. SIDDHIVINAYAK KULKARNI
Graduate School of Information Technology and
Mathematics University of Ballart AUSTRALIA
Dr. SIKANDAR HAYAT KHIYAL
Professor & Chairman DCS& DSE, Fatima
Jinnah Women University, Rawalpindi,
PAKISTAN
Dr. MUHAMMAD SHER
Professor & Chairman DCS, FBAS,
International Islamic University Islamabad,
PAKISTAN
Dr. ABDUL AZIZ
Professor of Computer Science, University of
Central Punjab, PAKISTAN
Dr. M. UMER KHAN
Asst. Professor Department of Mechatronics, Air
University Islamabad, PAKISTAN
iv
Journal of Theoretical and Applied Information Technology
January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
E-ISSN: 1817-3195
Dr. RIKTESH SRIVASTAVA
Assistant Professor, Information Systems
Skyline University College
P O Box 1797, Sharjah, UAE
Dr. BONNY BANERJEE
PhD in Computer Science and Engineering,
The Ohio State University, Columbus, OH, USA
Senior Scientist
Audigence, FL, USA
PROFESSOR NICKOLAS S. SAPIDIS
DME, University of Western Macedonia
Kozani GR-50100, GREECE.
Dr. NAZRI BIN MOHD NAWI
Software Engineering Department, Faculty of
Science Computer Information Technology,
Universiti Tun Hussein Onn
MALAYSIA
Dr. JOHN BABALOLA OLADOSU
Ladoke Akintola University of Technology,
Ogbomoso, NIGERIA
Dr. ABDELLAH IDRISSI
Department of Computer Science, Faculty of
Science, Mohammed V University - Agdal,
Rabat, MOROCCO
Dr. AMIT CHAUDHRY
University Institute of Engineering and
Technology, Panjab University, Sector-25,
Chandigarh, INDIA
Dr. ASHRAF IMAM
Aligarh Muslim University, Aligarh-INDIA
Dr. MOHAMMED ALI HUSSAIN
Dept. of Computer Science & Engineering, Sri
Sai Madhavi Institute of Science & Technology,
Mallampudi,
Rajahmundry, A.P, INDIA
Dr. KHALID HUSSAIN USMANI
Asst. Professor Department of Computer Science,
Arid Agriculture University,
Rawalpindi, PAKISTAN
Dr. GUFRAN AHAMD ANSARI
Qassim University, College of Computer Science,
Ministry of Higher Education,
Qassim University, KINGDOM OF SAUDI
ARABIA
Dr. Defa Hu
School of Information, Hunan University of
Commerce
Changsha 410205, Hunan, P. R. of China
Elite Panel Members Have A Decision Weight Equivalent of Two Referees (Internal OR External).
The Expertise Of Editorial Board Members Are Also Called In For Settling Refereed Conflict About
Acceptance/Rejection And Their Opinion Is Considered As Final.
v
Journal of Theoretical and Applied Information Technology
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© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
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E-ISSN: 1817-3195
PREFACE
Journal of Theoretical and Applied Information Technology (JATIT) published since 2005 (E-ISSN 18173195 / ISSN 1992-8645) is an International refereed research publishing journal with a focused aim of
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Journal of Theoretical and Applied Information Technology
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© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
E-ISSN: 1817-3195
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vii
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
E-ISSN: 1817-3195
COMPARING FUZZY LOGIC AND FUZZY C-MEANS (FCM)
ON SUMMARIZING INDONESIAN LANGUAGE DOCUMENT
1
DWI ADE RIANDAYANI, 2I KETUT GEDE DARMA PUTRA, 3PUTU WIRA BUANA
123
Department of Information Technology, Udayana University, Indonesia
E-mail: [email protected], [email protected], [email protected]
ABSTRACT
Text summarization is one way to get the information which is quite effective. Approach techniques of the
text summarization are classified into two, namely abstractive and extractive. This paper is focused on the
extractive techniques so that the output of the application is important sentences which are taken from the
original text intact without modification verbally. There are four majors processes in this research namely
preprocessing stage, scoring features, optimization of summarization by two methods (Fuzzy Logic and
Fuzzy C-Means), and extraction of summary results. This research use 7 features to calculate the score of
each sentence. The core of this research is to compare reliability Fuzzy logic and Fuzzy C-Means method in
optimizing process of summary results. The results showed that the method of Fuzzy Logic is
outperformed Fuzzy C-Means method with the closest similarity ratio to summarize manually by humans
with percentage accuracy of Fuzzy Logic is 58.25% while Fuzzy C-Means method is 54.33%. Increased
accuracy occurs when there is additional compression rate in the amount of 1.67% for Fuzzy Logic Method
and 4.5% for Fuzzy C-Means.
Keywords: Summarization, Text Features, Scores Sentences, Fuzzy Logic, Fuzzy C–Means
1.
INTRODUCTION
Text Summarization is very important and
useful technique to identify the topic of a text
quickly and accurately[1]. Summarization is the
process of condensing a source text into a shorter
version preserving its informationcontent[2]. The
goal is to help prospective readers save time and
effort in finding useful information in a text.
In a research, automatic text summarization
uses variety of methods and approaches[1]. The
previous research use the genetic algorithm as a
method to select the best sentence of text and use a
variety of combination feature amount. The result is
percentage of accuracy in each feature combination
[3]. Another research use Pseudo Genetic And
Probabilistic method for text summarization [4].
Techniques of text summarization can be
classified into two categories: extractive and
abstractive. Development of automatic text
summarization can apply one of the method or both
at once. Extractive summarization is technique of
sentence selection that represents the topic of a text
and incorporates it into the new text which is
shorter than the original, without modified from the
original text, while abstractive method produces a
summary with phrases that may not be found in the
original text [4]. This research use 3 kind of
summarizing: deductive, inductive and ineratif.
Deductive is a kind of finding mind topic in first
sentence of each paragraph. So the first sentence
have a biggest score among the following text
whole the paragraph. While the topic where located
in the end of each paragraph is named inductive and
topic where located in the centre of paragraph
named ineratif.
Process of summarizing includes four stages,
as follows: 1. text preprocessing (phase separation
process includes the sentence, the case folding, the
process of filtering the sentence, the word
tokenizing and stemming), 2. Feature Scoring, 3.
Sentence Selection (this stage optimized by using
Fuzzy Logic and Fuzzy C-Means), 4. Process of
preparing selected sentences. The research in this
thesis is more focused on a comparison of the
results of summarizing using the Fuzzy Logic and
Fuzzy C–Means thus, it will proof which method
will give better results of summarizing.
Fuzzy Logic is a knowledge representation
that is constructed by if-then rules. One key feature
of fuzzy rule based systems is their
comprehensibility because each fuzzy rule is
linguistically interpretable [5]. Characteristics of
Fuzzy Logic method are: 1. problem solving is
done by describing the system not by numbers, but
linguistically,
or
variables
that
contain
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Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
uncertainty/ambiguity , 2. Use of if-then rules to
clarify the relationship between one variable with
another, 3. Describe the system using fuzzy
algorithm [6]. While Fuzzy C-Means is a technique
of data clustering where the existence of each data
point in a cluster is determined by the degree of
membership.
This technique was first introduced by Jim
Bezdek in 1981 which then continues to develop
[7]. At first basic concept of FCM is to determine
cluster centers that will mark the location of the
average for each cluster. In the initial condition, the
cluster center is still not accurate. Each data point
has a degree of affiliation for each cluster. By
improving the cluster center and the degree of
affiliation of each data point repeatedly thus, it will
be seen that the cluster center will move towards
the right location. The Fuzzy C-Means clustering
algorithm is based on the minimization of an
objective function called C-means functional [8].
Both of these methods will be the focus of the test
in this paper.
2.
SUMMARIZATION APPROACH
2.1 Data Set and Preprocessing
Preprocessing is early stages to prepare
summarizing input text into data that is ready to be
processed. Stage that is contained in the text
preprocessing are segmentation paragraph, sentence
segmentation, folding case, sentence filtering,
tokenizing and stemming.
Paragraphs Segmentation is a limitation and
separation of text into paragraphs. Sentence
Segmentation is the separation paragraphs into
sentences. Characters besides the 'a- z' characters
are removed from the text and capital letters
converted to lowercase, this process is called case
folding. Each sentences have to be filtered from
stop word towards the text content. Tokenizing is
the stage to separate the sentence into its
constituent words using the character "white space"
as delimiter. In the end, the results of tokenizing
processes will be processed to the stage again to
stemming phase to get the base word of each word
[9] [10].
2.2 Selected Feature
After preprocessing, each sentence in the text
is represented by weight which is obtained from
scoring towards several features. Features are
carefully selected so that the system is able to
deliver approximately correct results. This paper
focus on seven features for each sentence. Each
feature will be worth between '0' and '1' [9].
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2.2.1
Sentence Position (F1)
Sentence position can determine whether the
sentence is important or not in representing the
content of text. In general, the first sentence in each
paragraph has the highest score is called the
deductive types of summarizing. But it is possible
that the main topic of the text is exist at the end of
paragraph it is named inductive summarizing.
Paragraph that the main topic is in the end of
paragraph named ineratif summarizing. In this
study, we use the three types of summarizing.
Value of the feature position is determined from the
results of division of this sentence position (X) on
each paragraph by the number of sentences of each
paragraph which is contained in the paragraphs (N).
Sentence Position can be calculated as Eq. 1.
(1)
2.2.2
Sentence Length Feature (F2)
Sentence length feature is used for filtering short
sentences that sometimes only contain names or
other authors. This short sentence is not expected to
be part of the summary. Calculation of the value of
this feature is the result of length sentences divided
with the number of words in a sentence (X) and the
number of the entire text (S).
(2)
2.2.3
Title feature (F3)
The words in the sentence that contain the title has
a high score. This stage calculates the suitability of
the words with content in the title. The title is
generally made up of several words, then the text of
the title text needs to undergo preprocessing. Then
token in the text checked against token title. Title
feature can be calculated as Eq. 3. Eq. 3.
(3)
2.2.4
Term Weight (F4)
Number of occurrences of the term in a sentence
can be used for detecting important phrase. Score
each sentence can be determined by summing the
scores of each constituent word. The term weight
Method calculates weights sentence so that this
method is referred as tf.isf (Term Frequency.
Inverse Sentence Frequency). Weight of each
sentence in the text is obtained by the
multiplication of the number of occurrences of the i
term in the text with its inverse value, this method
can be calculated as Eq. 4.
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(4)
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
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The next score for each sentence is calculated by
dividing the sum of all the scores contained in I
sentence to score maximum sentence on
mathematical text. The calculation can be seen in
equation 5.
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these methods, the following stages of the system is
shown in Figure 1.
(5)
2.2.5
Entity Word or term (F5)
Frequency of occurrence of the term or entity that
occurs in the source text can be become a factor to
calculate importance words. The word entity can be
a one's name, job title, name of the country and so
forth. Scores on this feature can be calculated from
the ratio of the number of entities/terms that appear
in the sentence to the sentence length.
(6)
2.2.6
Numerical Data (F6)
Sentences containing numerical data are assumed to
be the word that is quite important and most likely
have to be included into the results summary. Score
of features numerical data were calculated as the
ratio of the number of numerical data contained in
the sentence to sentence length.
(7)
2.2.7
Thematic Word (F7)
Thematic word is a word that appears quite
frequently in a text. This feature is important
because the words which contain thematic word can
be indicated as important sentences. The 10 words
with the highest frequency is used as thematic word
in this feature. Score calculation of thematic word
feature is the quotient between the number of words
in sentences with thematic maximum value of the
thematic word whole the sentence.
(8)
3.
Figure 1: System Step
From Figure 1, the results of the optimization phase
using Fuzzy Logic and Fuzzy C-Means is done
after preprocessing stage and scoring feature.
Success of automatic text summarizing is the
selection of sentence at this stage of the
optimization results [9]. Optimization phase will
produce sentences with high scores as a result of a
summary.
3.1 Optimizing with Fuzzy Logic
Shown earlier in Figure 1, that there are 3
processes in stages of Optimization of Fuzzy
Logic: Fuzzifier, Inference Engine and Defuzzifier.
Input on fuzzy logic method is score 7 feature of all
sentences. Enter the crisp fuzzification stage that
will be supposed to be into some fuzzy sets namely:
very low (SR), low (L), medium (M), high (H) and
very high (ST). Membership functions which are
used in here is "Curve Triangle" with a membership
limit shown in Table 1.
THE METHODOLOGHY
In this section explain how to extract the
summary result that will be optimized by using
Fuzzy Logic and Fuzzy C-Means method. As
explained in the Introduction, the core of this paper
is to compare the reliability of the two methods that
use in this research. Before describing more about
720
Table 1: Boundary Value of Fuzzy Logic Membership
SR
R
M
T
ST
a
0.1
b
0
c
0.2
0.3
0.6
0.8
0.25
0.5
0.75
1
0.4
0.7
0.9
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
Rules of Fuzzy Logic membership functions shown
in Eq. 9 and the affiliation function graph which is
shown in Figure 2.
(9)
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word (x5), Numerical Data (x6), Thematic Words
(x7).
FCM output is not fuzzy inference system, but
a row of cluster centers and several degree of
affiliation for each data point. This information can
be used to construct fuzzy inference system. Fuzzy
C-Means algorithm is as follows:
Step 1: Input data that will be clustered, X , in the
form of matrix n x m (n = number of data samples ,
m = attributes of each data = 70, where Xij = data
sample i (i = 1,2,…, n), attribute j (j = 1,2,…,7)
Step 2: Set the number of clusters = c = 3, Rank =
w = 2, Maximum iterations = 100, Error smallest
expected = 0.00001, initial objective function (P0)
= 0, the initial iteration (t) = 1.
Figure 2:Graph of Fuzzy Logic Membership
Degree of fuzzy logic affiliation are in the
range 0-1. Later, each sentence in the text will be
measured by degrees 7 features that have been
described previously.
In many studies, antecedent fuzzy sets
were generated and tuned by numerical input data
for rule base construction to improve the
classification accuracy of FRBCSs [11] [12]. The
most important part of inference engine is fuzzy IFThen rules [9]. The experience of the human
controller is usually expressed as some linguistic
"IF-THEN" rules that state in what situation(s)
which action(s) should be taken [13]. One example
of IF-THEN rule is as follows: IF (Position
sentence is Very High) and (Long sentence is Low)
and (Title Feature is High) and (Term Weight is
Medium) and (Noun Entities is Low) and ( Data
Numerical is Very High ) and ( Thematic Word is
Very High) THEN sentence is important.
Tsukamoto is a settlement that will be used in the
Inference Engine [14]. In the end of the stage,
defuzzifier crisp sentences are classified into
categories important, average and not important,
then the next set is converted into crisp numbers of
sentence weight.
3.2 Optimizing with Fuzzy C-Means
Optimization with Fuzzy C-Means method
will classify the input sentence into three clusters
include: cluster of important sentences, sentence of
the average cluster and cluster sentences are not
important. The input which is used in the form of
sentences and sentence attributes in form of
features. There are 7 Attributes that are used
namely: Position Sentence (x1), length of sentence
(x2), Title Feature (x3), Term Weight (x4), Entities
Step 3: Generate random numbers µik, i = 1,2,…,n;
k = 1,2,3; as elements initial partition matrix U.
Count the number of each column:
(10)
with i = 1,2,…,n
Calculate:
(11)
Step 4: Calculate cluster k: Vkj, with k = 1,2,3; and
j = 1,2,…,m.
(12)
Step 5: Calculate the objective function at iteration
t, Pt
(13)
Step 6: Calculate the change in the partition matrix
(14)
With: i = 1,2,…,n; and k = 1,2,…,m
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© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
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Step 7: Check the condition to stop. If the value of
(|Pt – Pt-1| < 0.00001) or (t > 100) then stop and
then specify the cluster affiliation. If not: t = t + 1,
repeat step 4.
E-ISSN: 1817-3195
Combination of recall and precision values then
generate f -measure.
(17)
3.3 Sentence Extraction
Extraction of Fuzzy Logic and Fuzzy CMeans will generate sentences with high scores in
descending order as a result of a summary.
Sentences were extracted and taken as much as
30% and 25% the text of the original text.
Possibilities of extraction is designed varied to
determine the composition of the best summaries
results of Fuzzy Logic and Fuzzy C-Means were
compared with the ideal of summarization by
humans. The sentence will be re-ordered according
to the actual order of the original text. In addition,
this study will also compare the results of
summarization is deductive, inductive, ineratif by
using a composition of the best extraction results
summaries. This study examined a sample of 20
Indonesian language text obtained from several
news sites are: kompas.com, kapanlagi.com and
yahoo.co.id.
4.
Table 2 shows the sample text that will be
processed by our system.
Table 2: Sample of Indonesian Text
Memaknai Kemerdekaan Di Hari Kemenangan
Jakarta - Ya, 68 tahun sudah Indonesia
merdeka. Sebagai Negara maritim dan kepulauan
terbesar di dunia, Indonesia juga memiliki ragam
etnis dan budaya terbanyak dibandingkan Negara
lain. Suatu kerukunan dalam keberagaman yang patut
dibanggakan sekaligus pencapaian yang tak mudah
dilakukan. Tentu, sepanjang perjalanan itu, banyak
kemajuan yang sudah dicapai, meski juga
menyisakan keberharapan. Salah satu yang perlu kita
renungkan adalah kesejahteraan bagi seluruh rakyat
Indonesia.
Jika kita merenungkan kembali cita-cita
bangsa yang dituangkan dalam pembukaan UndangUndang Dasar 1945, terlihat bahwa Indonesia adalah
bangsa yang besar. Dan oleh karenanya,
kemerdekaan harus diwujudkan untuk melindungi
segenap bangsa, memajukan kesejahteraan umum,
mencerdaskan kehidupan bangsa, sekaligus ikut
melaksanakan ketertiban dunia. Sebuah cita-cita
luhur yang diwariskan kepada kita untuk
mencapainya, bahkan di kala kondisi perekonomian
dunia sedang lesu.
Meski banyak lembaga ekonomi dunia
memuji pertumbuhan ekonomi Indonesia, namun
banyak hal membutuhkan pembenahan guna lebih
menguatkan ekonomi nasional. Sebagaimana yang
kita alami, harga berbagai kebutuhan pokok sudah
merangkak naik sejak awal Ramadhan, mencapai
puncaknya di hari Kemenangan. Atas berbagai
kenaikan harga ini, banyak saudara kita yang
merasakan ketidaknyamanan bahkan kesulitan dalam
menjalani hidupnya.
Satu hal yang pasti, Negara ini masih
membutuhkan pembenahan di berbagai bidang. Tentu
saja, seluruh masyarakat harus terlibat dalam
pembenahan, sekaligus memperkuat nasionalisme
yang ada dalam konsensus bersama: UUD 1945,
Pancasila, Negara Kesatuan Republik Indonesia, serta
Bhinneka Tunggal Ika. Yang harus kita lakukan
adalah menjalani peran masing-masing dalam
kehidupan bernegara: giat bekerja untuk membangun
bangsa, aktif mengawasi jalannya pemerintahan serta
memenuhi kewajiban perpajakan.
RESULT AND ANALYSIS
The main objective of this study was to
compare the reliability of the method of Fuzzy
Logic and Fuzzy C-Means in terms of optimization
of the results of summarization text in Indonesian
language. Therefore, we obtain two results, one for
summarization method (deductive, inductive,
inertif) and the other for extraction composition.
Research has also been done by summarizing the
20 samples Indonesian language text that comes
from multiple news sites.
Outcome evaluation system using the formula
precision, recall and f-measure which is called
intrinsic method [15]. Evaluation creates an ideal
set of summaries, one for each text to test the text
then comparing the results with a summary of the
ideal system summary. Measurement is in overlap
of content, it is often referred to recall and precision
sentence or phrase, but sometimes with a single
word overlap.
(15)
(16)
The content of Table 2 separated into each
sentence. White space character use as a delimiter
to separated text into sentences. The stop word is
also removed from sentence. Sentence then through
722
Journal of Theoretical and Applied Information Technology
31st January 2014. Vol. 59 No.3
© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
www.jatit.org
Table 5:Result of Comparison Compression Rate
Average F-measure
Optimizing
Method
30%
25%
Fuzzy Logic
0.47632
0.47572
Fuzzy C-Means
0.46759
0.45592
the filtering stage and thus case folding. Table 3
show the result.
Table 3: Separated Text into Sentences
No.
Sentences
Sentence
1.
jakarta 68 indonesia merdeka
2.
maritim kepulauan indonesia ragam etnis
budaya dibandingkan
3.
kerukunan
keberagaman
patut
dibanggakan pencapaian
4.
perjalanan kemajuan dicapai menyisakan
keberharapan
5.
renungkan kesejahteraan Indonesia
6.
merenungkan
cita
cita
bangsa
dituangkan pembukaan undang undang
1945 indonesia bangsa
7.
kemerdekaan diwujudkan melindungi
segenap
bangsa
memajukan
kesejahteraan mencerdaskan bangsa
melaksanakan ketertiban
8.
kemerdekaan diwujudkan melindungi
segenap
bangsa
memajukan
kesejahteraan mencerdaskan bangsa
melaksanakan ketertiban
9.
ekonomi memuji ekonomi indonesia
membutuhkan pembenahan menguatkan
ekonomi
10.
alami pokok merangkak ramadhan
puncaknya kemenangan
11.
saudara merasakan ketidaknyamanan
kesulitan hidupnya
12.
membutuhkan pembenahan
13.
terlibat
pembenahan
memperkuat
nasionalisme konsensus
uud 1945
pancasila kesatuan indonesia bhinneka
tunggal ika
14.
lakukan peran bernegara
giat
membangun bangsa aktif mengawasi
jalannya
memenuhi
kewajiban
perpajakan
E-ISSN: 1817-3195
Table 6 shows the f-measure Fuzzy Logic
outperformed Fuzzy-C Means and following by the
graphic show in figure 3. This result are calculated
with 30% compression rate.
Table 6:Result of Comparison F-Measure
Teks
Precission
Recall
F-measure
Accuration
1
0.2000
0.6667
0.3077
33.33%
2
0.0000
0.0000
0.0000
0
3
0.5000
0.6000
0.5455
60%
4
0.6000
0.7500
0.6667
75%
5
0.4000
0.6667
0.5000
33.33%
6
0.2500
0.3333
0.2857
33.33%
7
0.2000
0.3333
0.2500
33.33%
8
0.1429
0.2500
0.1818
75%
9
0.6667
1.0000
0.8000
100%
10
0.7500
1.0000
0.8571
100%
11
0.5000
1.0000
0.6667
100%
12
0.4000
0.5000
0.4444
50%
13
0.5000
1.0000
0.6667
100%
14
0.6667
1.0000
0.8000
50%
15
0.2500
0.5000
0.3333
50%
16
0.5000
0.6667
0.5714
67%
17
0.0000
0.0000
0.0000
0%
18
0.7500
1.0000
0.8571
100%
19
0.3333
0.5000
0.4000
50%
20
0.0000
0.0000
0.0000
0%
Average Accuration :
58.83%
The result of stemming for the sample shown on
Table 4 below.
Table 4: Stemming Result
No.
No.
Token
Token
Sentence
1
1
jakarta
2
1
68
3
1
indonesia
4
1
merdeka
5
2
maritime
1.0000
F-Measure
0.8000
Table 5 shows the f-measure comparison results
between the method of Fuzzy Logic and Fuzzy CMeans with the composition of the extraction of 40
%, 30 %, 25 % and 20 %.
723
0.6000
Fuzzy
Logic
0.4000
0.2000
0.0000
1 3 5 7 9 11 13 15 17 19
Fuzzy
CMeans
Figure 3:Graph of Comparison F-measure
Journal of Theoretical and Applied Information Technology
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© 2005 - 2014 JATIT & LLS. All rights reserved.
ISSN: 1992-8645
5.
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CONCLUSION
This paper have compared Fuzzy Logic and
Fuzzy C-Means as a method to optimize the
extraction of sentences in the automatic text
summarization. The research selected important
sentences based on 7 features: sentence position,
sentence length, feature title, term weight, entity
word or term, numerical data and thematic word.
The deductive type is use on summarization and
then this research applied to two sentences
compression rate was equal to: 30% and 25%. It
turned out the best combination on the compression
rate of the summary which is obtained by 30%.
From research obtained accuracy of 58.25% for the
Fuzzy Logic method and 55.5 % for Fuzzy CMeans method. Acuracy increase occur when there
is additional compression rate in the amount of
1.67% for Fuzzy Logic Method and 4.5% for Fuzzy
C-Means. Fuzzy Logic method was able to
optimize the results of summarizing text better than
the Fuzzy C-Means method in automatic text
summarization.
[9]
[10]
[11]
[12]
[13]
[14]
REFRENCES :
[1] H. P. Luhn, "The automatic creation of
literature abstracts," IBM J. Res. Dev., vol. 2,
pp. 159-165, 1958.
[2] Regina Barzilay, Michael Elhadad, "Using
lexical chains for text summarization,"
Proceedings of Intelligent Scalable Text
Summarization Workshop, 1997.
[3] Aristoteles.
"Pembobotan
Fitur
Pada
Peringkasan
Teks
Bahasa
Indonesia
Menggunakan Algoritma Genetika." Thesis,
Pertanian Bogor Institute, 2011.
[4] Albaraa Abuobieda M. Ali, et all., "Pseudo
Genetic And Probabilistic-Based Feature
Selection Method For Extractive Single
Document Summarization," JATIT, Vol. 32
No.1, 2011.
[5] O. Dehzangi Et. all., "Efficient fuzzy rule
generation: A new approach using data mining
principles and rule weighting," IEEE FSKD,
2007.
[6] Anto
Satriyo
Nugroho,
“Pengantar
Softcomputing,”
2003.
Available
at:
http://asnugroho.net/papers/ikcsc.pdf
[7] James C. Bezdek et all., "FCM: The Fuzzy
C-Means Clustering Algorithm," Computers
& Geosciences, Vol. 10, No. 2-3, pp. 191-203,
1984.
[8] Neha Jain and Seema Shukla, "Fuzzy
Database Using Extend Fuzzy C-Means
[15]
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E-ISSN: 1817-3195
Clustering," IJERA. Vol. 2, Issue 3, May-Jun
2012, pp.1444-1451.
Ladda Suanmali, et all., "Fuzzy Logic Based
Method for Improving Text Summarization,"
IJCSIS, Vol. 2, No. 1, 2009.
Talla, fadillah Z. "A Study of Stemming
Effects on Information Retrieval in Bahasa
Indonesia," Master of Logic Project. Institute
for Logic, Language and Computation.
Universiteit
van
Amsterdam.
The
Neatherlands.
D. Nauck and R. Kruse, “A neuro-fuzzy
method to learn fuzzy classification rules from
data,” Fuzzy Sets and Systems, pp. 277-288,
1997.
S. Abe and R. Thawonmas, “A fuzzy classifier
with ellipsoidal regions,” IEEE Trans. on
Fuzzy Systems, pp. 358-368, 1997.
Li-Xin Wang and M. Mendel, "Generating
Fuzzy Rules by Learning from Examples,"
IEEE Transaction on Systems, Man, and
Cybernetics, Vol.22, No. 6, 1992.
Kamble P.N, “FLC Modeling of Classical
EEG Signals Model by the Technique
Tsukamoto Fuzzy Rule Base,” International
Journal of Statistika and Mathematika.
Volume 7, Issue 3, 2013 pp 52-57.
Martin Hassel. “Evaluation of Automatic Text
Summarization,”
Stockholm.
Licentiate
Thesis. 2004.
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J ou r n a l of Th eor etica l a n d Applied In f or ma tion Tech n ology
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Subject Area: Computer Science | Mathematics
Subject Category: Computer Science (miscellaneous)
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, Theoretical Computer Science
Publisher: Asian Research Publishing Network (ARPN). Publication type: Journals. ISSN: 18173195, 19928645
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1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Data
2011
2012
0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,137 0,167
Total Documents
0
0
0
0
0
0
0
0
0
0
0
222
229
622
Total Docs. (3years)
0
0
0
0
0
0
0
0
0
0
0
0
222
451
Total References
0
0
0
0
0
0
0
0
0
0
0
Total Cites (3years)
0
0
0
0
0
0
0
0
0
0
0
0
61
Self Cites (3years)
0
0
0
0
0
0
0
0
0
0
0
0
4
17
Citable Docs. (3years)
0
0
0
0
0
0
0
0
0
0
0
0
222
451
Cites / Doc. (4years)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,27
0,39
Cites / Doc. (3years)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,27
0,39
Cites / Doc. (2years)
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,00
0,27
0,39
3.880 4.120 9.475
178
References / Doc.
0,00 0,00 0,00 0,00 0,00 0,00
0,00 0,00
0,00
0,00
17,48 17,99
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