A domain specific automatic text summarization using fuzzy logic
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This paper proposes a domain-specific automatic text summarization technique using fuzzy logic. It extracts important sentences from documents using both statistical and linguistic features. The technique first preprocesses documents by segmenting sentences, removing stop words, and stemming words. It then extracts eight features from each sentence, including title words, sentence length, position, and similarity. Fuzzy logic is used to assign an importance score to each sentence based on these features. Sentences are ranked and the top sentences are selected for the summary based on the compression rate. The technique was evaluated on news articles and shown to generate good quality summaries compared to other summarizers based on precision, recall, and F-measure.
![International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME
450
by choosing important sentences of the document. An Automatic text summarization works very
well on structured documents such as news articles, research publications and reports.
Text summarization has two approaches: Extraction and Abstraction. Extraction involves selecting
sentences of high relevance (rank) from the document based on word and sentence features and put
them together to generate summary. It uses mostly statistical methods. Abstraction procedure
examines the text, interprets it and generates summary using different sentences. It uses Linguistic
methods. This paper focuses on extractive summarization technique. It uses a combination of both
Statistical and Linguistic methods on fusion of various features to generate a better quality summary.
RELATED WORK
Since late 50s text summarization has been a crucial and important research area. The first
Automatic text summarization was created by Luhn in 1958[1] based on term frequency. Then G. J.
Rath, A. Resnick, and T. R. Savage[2] have proposed the evidences of problems in generating the
summaries using term frequency feature in 1961. Both studies are characterized by surface level
approaches. In late 60s, entity level approaches appeared: the first of its kind used syntactic analysis
proposed by Climenson [3].This was followed by Edmundson’s work[4] which used term
frequency, location features and cue words .Earliest instances of research on summarization was
done on scientific documents followed by various works published in other domains, mostly on
newswire data. In 1990s. with the advent of machine learning techniques in Natural Language
Processing, many publications came that used statistical techniques to produce document summaries.
They have used a combination of appropriate features and learning algorithms. Other approaches
have used hidden Markov models[5] and log-linear models to improve extractive summarization.
Recently, neural networks are used to generate summary for single documents using
extraction[6]. Very little work is done on automatic text summarization based on Artificial
Intelligence and evolutionary techniques. M.S.Binwale l[7] has designed automatic text
summarization using integrated hybrid model. He has used Diversity-based methods and Swarm
based methods followed by Fuzzy logic. Experimental results have shown that this model produces
good quality of summary.
Ladda Suanmali[8] in his work has used sentence weight ,a numerical measure assigned to
each sentence and then selecting sentences in descending order of their sentence weight for the
summary.
L.Antiqueira [9] has proposed a method for extractive summarization using concept of
complex networks and its metrics. It has shown that this method is capable of capturing important
text features as expected.
For MEDLINE citations, .an automatic summarization system has been introduced by
Marcelo Fiszman[10] . It is an domain-specific abstractive summarization which outperformed the
baseline summarizer considerably.
A lot of work has been done in single document and multi document summarization using
statistical methods. A lot of researchers are trying to apply this technology to a variety of new and
challenging areas, including multilingual summarization and multimedia news broadcast.
SURVEY ON NEED AND SCOPE OF TEXT SUMMARIZATION
Text Summarization is increasingly being used in the commercial sector such as
• Telephone communication industry, e.g BT’s ProSum.
• In data mining of text databases, E.g. Oracle’s Context.
• In filters for web-based information retrieval, E.g. Inxight’s summarizer used in Alta Vista
Discovery](https://image.slidesharecdn.com/adomain-specificautomatictextsummarizationusingfuzzylogic-130918075222-phpapp01/85/A-domain-specific-automatic-text-summarization-using-fuzzy-logic-2-320.jpg)
![International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME
451
• In word Processing tools e.g. Microsoft’s AutoSummarize
• A variety of new applications are using multilingual summarization, multimedia news
broadcast, audio scanning services for the blind etc.
• To summarize news to SMS or WAP-format for mobile phones.
Many approaches differ on the manner of their problem formulations.
A BETTER APPROACH TO SUMMARIZATION
This approach uses both statistical and Linguistic methods [11]to improve the quality of
generated summary. It uses Fuzzy logic for effective Text Summarization[12]. Fuzzy logic uses
decision module that determines the degree of importance of each sentence based on its rated
features. Decision module is designed using a fuzzy inference system.
This approach is illustrated in figure 4.1
Text summarization approach consists of following stages:
• Preprocessing
• Feature Extraction
• Fuzzy logic scoring
• Sentence selection and Assembly
PREPROCESSING
It has 4 steps:
Segmentation: It is a process of dividing a given document into sentences.
Removal of Stop words: Stop words are frequently occurring words such as ‘a’ an’, the’ that
provides less meaning and contains noise. The Stop words are predefined and stored in an array.
Tokenization and POS Tagging: A standard Parser cum Tagger is used to generate tokens and tag
them with proper parts of speech such as such as nouns(NN), verbs(VBZ), adjectives(JJ) and
adverbs(ADVB), determiners(DT) coordinating conjunction(CC) etc. It also groups syntactically
correlated words into phrases such as noun phrase, verb phrase, adjective phrase etc.
Word Stemming: converts every word into its root form by removing its prefix and suffix so that it
can be used for comparison with other words.
Preprocessing Feature
selection
Fuzzification
Rule Basedefuzzification
Selection of
sentences &
assembly
Summary
Document](https://image.slidesharecdn.com/adomain-specificautomatictextsummarizationusingfuzzylogic-130918075222-phpapp01/85/A-domain-specific-automatic-text-summarization-using-fuzzy-logic-3-320.jpg)
![International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME
452
FEATURE EXTRACTION
The text document is represented by set, D= {S1, S2,- - - , Sk} where, Si signifies a sentence
contained in the document D .The document is subjected to feature extraction. The important word
and sentence features to be used are decided .This work uses features such as Title word, Sentence
length, Sentence position, numerical data, Term weight, sentence similarity, existence of Thematic
words and proper Nouns .
1. Title word:
A high score is given to the sentence if it contains words occurring in the title as the main
content of the document is expressed via the title words. This feature is computed as follows:
If Nt is the number of words in the sentence that occur in the title and Ntotal is the total number of
words in the title, then
ࡲ ൌ
ܜۼ
ܔ܉ܜܗܜۼ
2. Sentence Length:
We eliminate the sentences which are too short such as datelines or author names. For every
sentence the normalized length of sentence is calculated as
۴ ൌ
ܚ܍܊ܕܝۼ ܗ ܛ܌ܚܗܟ ܑܖ ܍ܐܜ ܍܋ܖ܍ܜܖ܍ܛ
ܚ܍܊ܕܝۼ ܗ ܛ܌ܚܗܟ ܑܖ ܍ܐܜ ܜܛ܍ܖܗܔ ܍܋ܖ܍ܜܖ܍ܛ
3. Sentence Position:
The sentences occurring first in the paragraph have highest score. Suppose a paragraph has n
sentences then the score of every sentence for this feature is calculated as follows:
F3(S1) = n/n; F3(S2)=4/5; F3(S3)=3/5; F3(S4)=2/5; and so on.
4. Numerical data:
The sentences having numerical data can reflect important statistics of the document and may
be selected for summary. Its score is calculated as:
۴ሺܑ܁ሻ ൌ
ܚ܍܊ܕܝۼ ܗ ܔ܉܋ܑܚ܍ܕܝܖ ܉ܜ܉܌ ܑܖ ܍ܐܜ ܍܋ܖ܍ܜܖ܍ܛ ܑ܁
܍܋ܖ܍ܜܖ܍܁ ܐܜܖ܍ۺ
5. Thematic words:
These are domain specific words with maximum possible relativity. The score for this feature
is calculated as the ratio of the number of thematic words that occurs in a sentence over the
maximum number of thematic words in a sentence.
۴ሺܑ܁ሻ ൌ
ܚ܍܊ܕܝۼ ܗ ܋ܑܜ܉ܕ܍ܐ܂ ܉ܜ܉܌ ܑܖ ܍ܐܜ ܍܋ܖ܍ܜܖ܍ܛ ܑ܁
ܠ܉ۻ ܗܖ ܗ ܋ܑܜ܉ܖ܍ܐܜ ܛ܌ܚܗܟ
6. Sentence to Sentence Similarity:
For each sentence S, the similarity between S and every other sentence is computed by the
method of token matching. The [N][N] matrix is formed where N is the total number of sentence in a
document. The diagonal elements of a matrix are set to zero as the sentence should not be compared
with itself. The similarity of each sentence pair is calculated as follows](https://image.slidesharecdn.com/adomain-specificautomatictextsummarizationusingfuzzylogic-130918075222-phpapp01/85/A-domain-specific-automatic-text-summarization-using-fuzzy-logic-4-320.jpg)
![International Journal of Computer Engineering and Technology (IJCET), ISSN 0976-
6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME
453
ࡲ ൌ
∑ሾSimሺSi, ሺSjሻሿ
MaxሾSimሺSi, ሺSjሻሿ
Where i=1 to N and j=1 to N.
7. Term weight:
The score of this feature is given by the ratio of summation of term frequencies of all terms in
a sentence over the maximum of summation values of all sentences in a document.
It is calculated by the following equation.
F7=∑TFI
--------------- Where i=1 to n, n is the number of terms in a sentence.
MAX(∑TFI )
8. Proper Nouns:
The sentence that contains maximum number of proper nouns is considered to be important.
Its score is given by
F8= Number of proper nouns in the sentence s
--------------------------------------------------
Sentence length(s)
Thus each sentence is associated with 8 feature vector. Using all the 8 feature scores, the
score for each sentence are derived using fuzzy logic method. The fuzzy logic method uses the fuzzy
rules and triangular membership function .The fuzzy rules are in the form of IF-THEN .The
triangular membership function fuzzifies each score into one of 3 values that is LOW,MEDIUM &
HIGH. Then we apply fuzzy rules to determine whether sentence is unimportant, average or
important. This is also known as defuzzification.
For example IF (F1is H) and (F2 is M) and (F3 is H) and (F4 is M) and (F5 is M) and (F6 is
M) and (F7 is H) and (F8 is H) THEN (sentence is important).
All the sentences of a document are ranked in a descending order based on their scores. Top n
sentences of highest score are extracted as document summary based on compression rate. Finally
the sentences in summary are arranged in the order they occur in the original document.
EVALUATION METHODOLOGY
The evaluation of the summaries is done based on two factors mentioned in Fig. 5. We used
2 documents from news articles belonging to technical domain as an input to the system. Here the
human generated summaries are used as reference summaries for evaluation of our results. The
human generated summary acts as a reference summary since humans can capture and relate deep
meanings of the text as compared to machines. We received human generated summaries for our
input documents from different Experts. Here we call the summaries of Fuzzy summarizer, online
summarizer 1,online summarizer 2 as the candidate summaries.
The performance of the proposed approach will be evaluated using precision, recall and F-
measure[12]. Precision evaluates the proportion of correctness for the sentences in the summary
whereas recall is utilized to evaluate the proportion of relevant sentences included in the summary.](https://image.slidesharecdn.com/adomain-specificautomatictextsummarizationusingfuzzylogic-130918075222-phpapp01/85/A-domain-specific-automatic-text-summarization-using-fuzzy-logic-5-320.jpg)
A domain specific automatic text summarization using fuzzy logic
- 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 449 A DOMAIN-SPECIFIC AUTOMATIC TEXT SUMMARIZATION USING FUZZY LOGIC 1 Mrs. A.R.Kulkarni Assistant Professor, Computer Science & Engg Department, Walchand Institute of Technology, Solapur 2 Dr. Mrs. S.S.Apte HEAD, Computer Science & Engg Department, Walchand Institute of Technology, Solapur ABSTRACT The amount of information on World Wide Web has increased enormously. In this context there is a need for text summarization. It creates summaries of the documents that consist of important sentences in the document. The summaries help the readers to make decision as to read the whole document or not thus acting as a time saver. Various Techniques have been proposed for text summarization by researchers that can be broadly classified into two types: Extraction and Abstraction. This Paper focuses on Text Summarization by Extraction using Fuzzy Logic.. Many Automatic text Summarization techniques have used either Statistics or Linguistics. Very Few works has used a combination of both. Our Paper uses the idea of both Statistical and Linguistic methods. This hybrid approach has been applied to news article dataset in the domain of technical news and we have evaluated their performances by using precision and recall method. It is found that this method generates good quality of summary. Keywords: Summarization, Statistics, Linguistics, fuzzifier, Defuzzifier, Rule-Base, Extraction. INTRODUCTION “Text Summarization” is a process of creating a shorter version of original text that contains the important information. The amount of information on the web is growing day by day. A considerable amount of time is wasted in searching for relevant documents. Hence text summarization technique came into existence which created a short summary for the text document INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY (IJCET) ISSN 0976 – 6367(Print) ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), pp. 449-461 © IAEME: www.iaeme.com/ijcet.asp Journal Impact Factor (2013): 6.1302 (Calculated by GISI) www.jifactor.com IJCET © I A E M E
- 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 450 by choosing important sentences of the document. An Automatic text summarization works very well on structured documents such as news articles, research publications and reports. Text summarization has two approaches: Extraction and Abstraction. Extraction involves selecting sentences of high relevance (rank) from the document based on word and sentence features and put them together to generate summary. It uses mostly statistical methods. Abstraction procedure examines the text, interprets it and generates summary using different sentences. It uses Linguistic methods. This paper focuses on extractive summarization technique. It uses a combination of both Statistical and Linguistic methods on fusion of various features to generate a better quality summary. RELATED WORK Since late 50s text summarization has been a crucial and important research area. The first Automatic text summarization was created by Luhn in 1958[1] based on term frequency. Then G. J. Rath, A. Resnick, and T. R. Savage[2] have proposed the evidences of problems in generating the summaries using term frequency feature in 1961. Both studies are characterized by surface level approaches. In late 60s, entity level approaches appeared: the first of its kind used syntactic analysis proposed by Climenson [3].This was followed by Edmundson’s work[4] which used term frequency, location features and cue words .Earliest instances of research on summarization was done on scientific documents followed by various works published in other domains, mostly on newswire data. In 1990s. with the advent of machine learning techniques in Natural Language Processing, many publications came that used statistical techniques to produce document summaries. They have used a combination of appropriate features and learning algorithms. Other approaches have used hidden Markov models[5] and log-linear models to improve extractive summarization. Recently, neural networks are used to generate summary for single documents using extraction[6]. Very little work is done on automatic text summarization based on Artificial Intelligence and evolutionary techniques. M.S.Binwale l[7] has designed automatic text summarization using integrated hybrid model. He has used Diversity-based methods and Swarm based methods followed by Fuzzy logic. Experimental results have shown that this model produces good quality of summary. Ladda Suanmali[8] in his work has used sentence weight ,a numerical measure assigned to each sentence and then selecting sentences in descending order of their sentence weight for the summary. L.Antiqueira [9] has proposed a method for extractive summarization using concept of complex networks and its metrics. It has shown that this method is capable of capturing important text features as expected. For MEDLINE citations, .an automatic summarization system has been introduced by Marcelo Fiszman[10] . It is an domain-specific abstractive summarization which outperformed the baseline summarizer considerably. A lot of work has been done in single document and multi document summarization using statistical methods. A lot of researchers are trying to apply this technology to a variety of new and challenging areas, including multilingual summarization and multimedia news broadcast. SURVEY ON NEED AND SCOPE OF TEXT SUMMARIZATION Text Summarization is increasingly being used in the commercial sector such as • Telephone communication industry, e.g BT’s ProSum. • In data mining of text databases, E.g. Oracle’s Context. • In filters for web-based information retrieval, E.g. Inxight’s summarizer used in Alta Vista Discovery
- 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 451 • In word Processing tools e.g. Microsoft’s AutoSummarize • A variety of new applications are using multilingual summarization, multimedia news broadcast, audio scanning services for the blind etc. • To summarize news to SMS or WAP-format for mobile phones. Many approaches differ on the manner of their problem formulations. A BETTER APPROACH TO SUMMARIZATION This approach uses both statistical and Linguistic methods [11]to improve the quality of generated summary. It uses Fuzzy logic for effective Text Summarization[12]. Fuzzy logic uses decision module that determines the degree of importance of each sentence based on its rated features. Decision module is designed using a fuzzy inference system. This approach is illustrated in figure 4.1 Text summarization approach consists of following stages: • Preprocessing • Feature Extraction • Fuzzy logic scoring • Sentence selection and Assembly PREPROCESSING It has 4 steps: Segmentation: It is a process of dividing a given document into sentences. Removal of Stop words: Stop words are frequently occurring words such as ‘a’ an’, the’ that provides less meaning and contains noise. The Stop words are predefined and stored in an array. Tokenization and POS Tagging: A standard Parser cum Tagger is used to generate tokens and tag them with proper parts of speech such as such as nouns(NN), verbs(VBZ), adjectives(JJ) and adverbs(ADVB), determiners(DT) coordinating conjunction(CC) etc. It also groups syntactically correlated words into phrases such as noun phrase, verb phrase, adjective phrase etc. Word Stemming: converts every word into its root form by removing its prefix and suffix so that it can be used for comparison with other words. Preprocessing Feature selection Fuzzification Rule Basedefuzzification Selection of sentences & assembly Summary Document
- 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 452 FEATURE EXTRACTION The text document is represented by set, D= {S1, S2,- - - , Sk} where, Si signifies a sentence contained in the document D .The document is subjected to feature extraction. The important word and sentence features to be used are decided .This work uses features such as Title word, Sentence length, Sentence position, numerical data, Term weight, sentence similarity, existence of Thematic words and proper Nouns . 1. Title word: A high score is given to the sentence if it contains words occurring in the title as the main content of the document is expressed via the title words. This feature is computed as follows: If Nt is the number of words in the sentence that occur in the title and Ntotal is the total number of words in the title, then ࡲ ൌ ܜۼ ܔ܉ܜܗܜۼ 2. Sentence Length: We eliminate the sentences which are too short such as datelines or author names. For every sentence the normalized length of sentence is calculated as ۴ ൌ ܚ܍܊ܕܝۼ ܗ ܛ܌ܚܗܟ ܑܖ ܍ܐܜ ܍܋ܖ܍ܜܖ܍ܛ ܚ܍܊ܕܝۼ ܗ ܛ܌ܚܗܟ ܑܖ ܍ܐܜ ܜܛ܍ܖܗܔ ܍܋ܖ܍ܜܖ܍ܛ 3. Sentence Position: The sentences occurring first in the paragraph have highest score. Suppose a paragraph has n sentences then the score of every sentence for this feature is calculated as follows: F3(S1) = n/n; F3(S2)=4/5; F3(S3)=3/5; F3(S4)=2/5; and so on. 4. Numerical data: The sentences having numerical data can reflect important statistics of the document and may be selected for summary. Its score is calculated as: ۴ሺܑ܁ሻ ൌ ܚ܍܊ܕܝۼ ܗ ܔ܉܋ܑܚ܍ܕܝܖ ܉ܜ܉܌ ܑܖ ܍ܐܜ ܍܋ܖ܍ܜܖ܍ܛ ܑ܁ ܍܋ܖ܍ܜܖ܍܁ ܐܜܖ܍ۺ 5. Thematic words: These are domain specific words with maximum possible relativity. The score for this feature is calculated as the ratio of the number of thematic words that occurs in a sentence over the maximum number of thematic words in a sentence. ۴ሺܑ܁ሻ ൌ ܚ܍܊ܕܝۼ ܗ ܋ܑܜ܉ܕ܍ܐ܂ ܉ܜ܉܌ ܑܖ ܍ܐܜ ܍܋ܖ܍ܜܖ܍ܛ ܑ܁ ܠ܉ۻ ܗܖ ܗ ܋ܑܜ܉ܖ܍ܐܜ ܛ܌ܚܗܟ 6. Sentence to Sentence Similarity: For each sentence S, the similarity between S and every other sentence is computed by the method of token matching. The [N][N] matrix is formed where N is the total number of sentence in a document. The diagonal elements of a matrix are set to zero as the sentence should not be compared with itself. The similarity of each sentence pair is calculated as follows
- 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 453 ࡲ ൌ ∑ሾSimሺSi, ሺSjሻሿ MaxሾSimሺSi, ሺSjሻሿ Where i=1 to N and j=1 to N. 7. Term weight: The score of this feature is given by the ratio of summation of term frequencies of all terms in a sentence over the maximum of summation values of all sentences in a document. It is calculated by the following equation. F7=∑TFI --------------- Where i=1 to n, n is the number of terms in a sentence. MAX(∑TFI ) 8. Proper Nouns: The sentence that contains maximum number of proper nouns is considered to be important. Its score is given by F8= Number of proper nouns in the sentence s -------------------------------------------------- Sentence length(s) Thus each sentence is associated with 8 feature vector. Using all the 8 feature scores, the score for each sentence are derived using fuzzy logic method. The fuzzy logic method uses the fuzzy rules and triangular membership function .The fuzzy rules are in the form of IF-THEN .The triangular membership function fuzzifies each score into one of 3 values that is LOW,MEDIUM & HIGH. Then we apply fuzzy rules to determine whether sentence is unimportant, average or important. This is also known as defuzzification. For example IF (F1is H) and (F2 is M) and (F3 is H) and (F4 is M) and (F5 is M) and (F6 is M) and (F7 is H) and (F8 is H) THEN (sentence is important). All the sentences of a document are ranked in a descending order based on their scores. Top n sentences of highest score are extracted as document summary based on compression rate. Finally the sentences in summary are arranged in the order they occur in the original document. EVALUATION METHODOLOGY The evaluation of the summaries is done based on two factors mentioned in Fig. 5. We used 2 documents from news articles belonging to technical domain as an input to the system. Here the human generated summaries are used as reference summaries for evaluation of our results. The human generated summary acts as a reference summary since humans can capture and relate deep meanings of the text as compared to machines. We received human generated summaries for our input documents from different Experts. Here we call the summaries of Fuzzy summarizer, online summarizer 1,online summarizer 2 as the candidate summaries. The performance of the proposed approach will be evaluated using precision, recall and F- measure[12]. Precision evaluates the proportion of correctness for the sentences in the summary whereas recall is utilized to evaluate the proportion of relevant sentences included in the summary.
- 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 454 For precision, the higher the values, the better the system is in omitting irrelevant sentences. Similarly, the higher the recall values the more successful the system would be in fetching the relevant sentences. The weighted harmonic mean of precision and recall is called as F-measure. The detail formula for Precision, recall and F-measure is as shown below. Precision = │ {Retrieved sentences} ∩ {Relevant sentences}│ ------------------------------------------------------------- │ {Retrieved Sentences} │ Recall= │ {Retrieved sentences} ∩ {Relevant sentences} │ __________________________________________ │ {relevant sentences} │ F-measure= 2 x ୰ୣୡ୧ୱ୧୭୬ ୶ ୖୣୡୟ୪୪ ୰ୣୡ୧ୱ୧୭୬ାୖୣୡୟ୪୪ EXPERIMENTAL RESULTS The two sports news articles , their manual summaries, summaries generated by our algorithm and summaries generated by two online summarizers are shown below. The chart showing the comparision between results of online summarizers and our proposed summarizer. Original Document 1
- 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 455 Original Document 2 Manual summary for Document 1
- 8. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 456 Manual summary for Document2 For document 1, the summary generated by our algorithm is:
- 9. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 457 For document 2, the summary generated by our algorithm is: Online summarizer1 • Document 1
- 10. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 458 • Document 2 Online Summarizer 2 • Document 1
- 11. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 459 • Document 2 Comparison Graphs • Document 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Summarizer 1 Summarizer 2 Our Summarizer Precision Recall f-measure
- 12. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 460 Document 2 CONCLUSION Automatic summarization is a complex task that consists of several sub-tasks. Each of the sub-task directly affects the ability to generate high quality summaries. In extraction based summarization the important part of the process is the identification of important relevant sentences of text. Use of fuzzy logic as a summarization sub-task improved the quality of summary by a great amount. The results are clearly visible in the comparison graphs. Our algorithm shows better results as compared to the output produced by two online summarizers. FUTURE SCOPE The quality of summary can still be improved by using topic segmentation and semantic analysis of the text in addition to the features considered above. We applied our method for single document summarization which could be extended for multiple document summarizations. REFERENCES 1. LUHN. H.P.1958. “Automatic Creation of Literature abstracts”,IBM Journal of Research & Development 2 April p-159. 2. G.J.Rath, A Rensick and T.R.Savage “The formation of abstracts by selection of sentences”, at IBM Foundation, Yorktown Heights, New York. 3. Climenson, W.D., Hardwick, N.H., Jacobson, S.N. (1961).”Automatic Syntax Analysis in Machine Indexing and Abstracting”. 4. Edmundson, H.P. (1969).New Methods in Automatic Extracting. 0 0.2 0.4 0.6 0.8 1 Summarizer 1 Summarizer 2 Our Summarizer Precision Recall f-measure
- 13. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976- 6367(Print), ISSN 0976 – 6375(Online) Volume 4, Issue 4, July-August (2013), © IAEME 461 5. M. Suneeta & S.Sameen Fatima “Corpus based Automatic Text Summarization System with HMM Tagger” at IJSCE ISSN: 2231- 2307,Volume-1, Issue-3, July 2011 6. Kaikhah.K “Automatic Text Summarization using neural networks” at Intelligent systems 2004 Proceedings,2004 2nd International IEEE Conference Volume 1. 7. Binwahlan.M.S., Salim.N.,& Suanmali.L (2009d),”Fuzzy Swarm based text summarization”, Journal of Computer Science, 5(5), 338-346 8. LaddaSuanmali, NaomieSalimand Mohammed Salem Binwahlan, “Fuzzy Logic Based Method for Improving Text Summarization”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 2, No. 1, 2009 9. L. Antiqueira, O. N. Oliveira Jr., L. F. Costa, and M. G. V. Nunes.”A complex network approach to text summarization “at Information Sciences 179(5):584-599 (2009) ”. 10. Marcelo Fiszman,Thomas.C.Rendflesh, Halil Kilicoglu “Abstraction summarization for managing the biomedical research literature”,CLS '04 Proceedings of the HLT-NAACL Workshop on Computational Lexical Semantics Pages76-83 Association for Computational Linguistics Stroudsburg, PA, USA ©2004. 11. Rushdi Shams, M.M.A. Hashem, Afrina Hossain, Suraiya Rumana Akter, and Monika Gope,”A corpus based web document summarization using statistical & Linguistic approach”, 12. Ladda Suanmali , Naomie Salim and Mohammed Salem Binwahlan, “Improving Text Summarization using Fuzzy Logic”, (IJCSIS) International Journal of Computer Science and Information ecurity, Vol. 2, No. 1, 2009. 13. Meghana.N.Ingole, M.S.Bewoor and S.H.Patil, “Context Sensitive Text Summarization using Hierarchical Clustering Algorithm”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, 2012, pp. 322 - 329, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 14. Roma V J, M S Bewoor and Dr.S.H.Patil, “Automation Tool for Evaluation of the Quality of NLP Based Text Summary Generated Through Summarization and Clustering Techniques by Quantitative and Qualitative Metrics”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 77 - 85, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. 15. V.Sujatha, K.Sriraman, K. Ganapathi Babu and B.V.R.R.Nagrajuna, “Testing and Test Case Generation by using Fuzzy Logic and N.L.P Techniques”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 3, 2013, pp. 531 - 538, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.