Pseudo genetic and probabilistic-based feature selection method for extractive single document summarization

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dc.contributor.authorAlbaraa Abuobieda M. ALI
dc.contributor.authorNaomie Salim
dc.contributor.authorRihab Eltayeb Ahmed
dc.contributor.authorMohammed Salem Binwahlan
dc.contributor.authorLadda Suanmali
dc.contributor.authorAhmed Hamza
dc.contributor.correspondenceA. A. M. ALI; Faculty of Computer Science and Information Systems, University Technology Malaysia, 81310, Johor, Malaysia; email: albarraa@hotmail.com
dc.date.accessioned2025-03-10T07:37:41Z
dc.date.available2025-03-10T07:37:41Z
dc.date.issued2011
dc.description.abstractText features, as a scoring mechanism, are used to identify the key ideas in a given document to be represented in the text summary. Considering all features within same the level of importance may lead to generate a summary with low quality. In this paper, we present a feature selection method using (pseudo) Genetic probabilistic-based Summarization (PGPSum) model for extractive single document summarization. The proposed method, working as features selection mechanism, is used to extract the weights of features from texts. Then, the weights will be used to tune features' scores in order to optimize the summarization process. In this way, important sentences will be selected for representing the document summary. The results show that, our PGPSum model outperformed Ms-Word and Copernic summarizers benchmarks by obtaining a similarity ratio closest to human benchmark summary. © 2005 - 2011 JATIT & LLS. All rights reserved.
dc.identifier.citationJournal of Theoretical and Applied Information Technology
dc.identifier.issn19928645
dc.identifier.scopus2-s2.0-80054738305
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5021
dc.languageEnglish
dc.publisherAsian Research Publishing Network (ARPN)
dc.rights.holderScopus
dc.subjectBinary selection
dc.subjectFeatures weights
dc.subjectGenetic
dc.subjectProbabilistic
dc.subjectSentence score
dc.subjectSimilarity
dc.subjectSummarization
dc.subjectText features
dc.titlePseudo genetic and probabilistic-based feature selection method for extractive single document summarization
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-80054738305&partnerID=40&md5=96c1d3ba0e00bfac7e64a8954cb288cd
oaire.citation.endPage87
oaire.citation.issue1
oaire.citation.startPage80
oaire.citation.volume32
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