Fuzzy genetic semantic based text summarization

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dc.contributor.authorLadda Suanmali
dc.contributor.authorNaomie Salim
dc.contributor.authorMohammed Salem Binwahlan
dc.contributor.correspondenceL. Suanmali; Faculty of Science and Technology, Suan Dusit Rajabhat University, Bangkok, 10300, Thailand; email: ladda_sua@dusit.ac.th
dc.date.accessioned2025-03-10T07:37:41Z
dc.date.available2025-03-10T07:37:41Z
dc.date.issued2011
dc.description.abstractAutomatic text summarization is a data reduction process to exclude unnecessary details and present important information in a shorter version. One way to summarize document is by extracting important sentences in the document. To select suitable sentences, a numerical rank is assigned to each sentence based on a sentence scoring approach. Highly ranked sentences are used for the summary. This paper proposed an automatic text summarization approach based on sentence extraction using fuzzy logic, genetic algorithm, semantic role labeling and their combinations to generate high quality summaries. This study explored the benefits of the genetic algorithm in the optimization problem in for feature selection during the training phase and adjusts feature weights during the testing phase. Fuzzy IF-THEN rules were used to balance the weights between important and unimportant features. Conventional extraction methods cannot capture semantic relations between concepts in a text. Therefore, this research investigates the use of the semantic role labeling to capture the semantic contents in sentences and incorporate it into the summarization method. This paper is evaluated in terms of performance using ROUGE toolkit. Experimental results showed that the summaries produced by the proposed approaches are better than other approaches produced by Microsoft Word 2007, Copernic Summarizer, and MANYASPECTS summarizers. © 2011 IEEE.
dc.identifier.citationProceedings - IEEE 9th International Conference on Dependable, Autonomic and Secure Computing, DASC 2011
dc.identifier.doi10.1109/DASC.2011.192
dc.identifier.isbn978-076954612-4
dc.identifier.scopus2-s2.0-84856110740
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5034
dc.languageEnglish
dc.rights.holderScopus
dc.subjectFuzzy logic
dc.subjectGenetic algorithm
dc.subjectSemantic role labeling
dc.subjectSentence extraction
dc.subjectStatistical method
dc.subjectText summarization
dc.titleFuzzy genetic semantic based text summarization
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84856110740&doi=10.1109%2fDASC.2011.192&partnerID=40&md5=ac9ab1986484dfa74143b3cee26c5ebc
oaire.citation.endPage1191
oaire.citation.startPage1184
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