SRL-GSM: A hybrid approach based on semantic role labeling and general statistic method for text summarization

Simple item page
dc.contributor.authorL. Suanmali
dc.contributor.authorN. Salim
dc.contributor.authorM.S. Binwahlan
dc.date.accessioned2025-03-10T07:37:41Z
dc.date.available2025-03-10T07:37:41Z
dc.date.issued2010
dc.description.abstractSentence extraction techniques are commonly used to produce extraction summaries. The goal of text summarization based on extraction approach is to identify the most important set of sentences for the overall understanding of a given document. One of the methods to obtain suitable sentences is to assign some numerical measure of a sentence for summary called sentence weighting and then select the best ones. In this study, we propose Semantic Role Labeling (SRL) approach to improve the quality of the summary created by the general statistic method. We calculate a couple of sentence semantic similarity based on the similarity of the pair of words using WordNet thesaurus to discover the word relationship between sentences. We perform text summarization based on General Statistic Method (GSM) and then combine it with the SRL method. We compare our results with the baseline summarizer and Microsoft Word 2007 summarizers. The results show that SRL-GSM and GSM give the best average precision, recall and f-measure for creation of summaries. © 2010 AsianNetwork for Scientific Information.
dc.identifier.citationJournal of Applied Sciences
dc.identifier.doi10.3923/jas.2010.166.173
dc.identifier.issn18125654
dc.identifier.scopus2-s2.0-74349126910
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/5038
dc.languageEnglish
dc.publisherAsian Network for Scientific Information
dc.rights.holderScopus
dc.subjectSemantic similarity
dc.subjectText summarization sentence extrzction
dc.titleSRL-GSM: A hybrid approach based on semantic role labeling and general statistic method for text summarization
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-74349126910&doi=10.3923%2fjas.2010.166.173&partnerID=40&md5=501989c593a973818223d433b6a511b0
oaire.citation.endPage173
oaire.citation.issue3
oaire.citation.startPage166
oaire.citation.volume10
Files
Collections
SCOPUS 2010-2014