Minimum redundancy and maximum relevance for single and multi-document Arabic text summarization

Simple item page
dc.contributor.authorOufaida, Houda
dc.contributor.authorNouali, Omar
dc.contributor.authorBlache, Philippe
dc.date.accessioned2023-10-04T18:55:27Z
dc.date.available2023-10-04T18:55:27Z
dc.date.issued2014-12
dc.description.abstractAutomatic text summarization aims to produce summaries for one or more texts using machine techniques. In this paper, we propose a novel statistical summarization system for Arabic texts. Our system uses a clustering algorithm and an adapted discriminant analysis method: mRMR (minimum redundancy and maximum relevance) to score terms. Through mRMR analysis, terms are ranked according to their discriminant and coverage power. Second, we propose a novel sentence extraction algorithm which selects sentences with top ranked terms and maximum diversity. Our system uses minimal language-dependant processing: sentence splitting, tokenization and root extraction. Experimental results on EASC and TAC 2011 MultiLingual datasets showed that our proposed approach is competitive to the state of the art systems.
dc.identifier.doihttps://doi.org/10.1016/j.jksuci.2014.06.008
dc.identifier.issn1319-1578
dc.identifier.urihttps://dl.cerist.dz/handle/CERIST/982
dc.publisherElsevier
dc.relation.ispartofseriesJournal of King Saud University - Computer and Information Sciences; 26(4)
dc.relation.pages450-461
dc.structureWeb Sémantique et Langue Arabe
dc.subjectArabic text summarization
dc.subjectSentence extraction
dc.subjectmRMR
dc.subjectMinimum redundancy
dc.subjectMaximum relevance
dc.titleMinimum redundancy and maximum relevance for single and multi-document Arabic text summarization
dc.typeArticle
Files
Collections
International Journal Papers