Multi-document summarization based on sentence cluster using non-negative matrix factorization

Yang, L; Cai, X; Pan, S; Dai, H; Mu, D

Multi-document summarization based on sentence cluster using non-negative matrix factorization

Yang, L Cai, X Pan, S

Dai, H Mu, D

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Publication Type:: Journal Article
Citation:: Journal of Intelligent and Fuzzy Systems, 2017, 33 (3), pp. 1867 - 1879
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Yang, L	en_US
dc.contributor.author	Cai, X	en_US
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X	en_US
dc.contributor.author	Dai, H	en_US
dc.contributor.author	Mu, D	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Journal of Intelligent and Fuzzy Systems, 2017, 33 (3), pp. 1867 - 1879	en_US
dc.identifier.issn	1064-1246	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127667
dc.description.abstract	© 2017 - IOS Press and the authors. All rights reserved. Multi-document summarization aims to produce a concise summary that contains salient information from a set of source documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence cluster using Nonnegative Matrix Tri-Factorization (NMTF). The proposed framework employs NMTF to cluster sentences using inter-type relationships among documents, sentences and terms, and incorporate the intra-type information through manifold regularization. The most informative sentences are selected from each sentence cluster to form the summary. When evaluated on the DUC2004 and TAC2008 datasets, the performance of the proposed framework is comparable with that of the top three systems.	en_US
dc.relation.ispartof	Journal of Intelligent and Fuzzy Systems	en_US
dc.relation.isbasedon	10.3233/JIFS-161613	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multi-document summarization based on sentence cluster using non-negative matrix factorization	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	33	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	33	en_US

Abstract:

© 2017 - IOS Press and the authors. All rights reserved. Multi-document summarization aims to produce a concise summary that contains salient information from a set of source documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence cluster using Nonnegative Matrix Tri-Factorization (NMTF). The proposed framework employs NMTF to cluster sentences using inter-type relationships among documents, sentences and terms, and incorporate the intra-type information through manifold regularization. The most informative sentences are selected from each sentence cluster to form the summary. When evaluated on the DUC2004 and TAC2008 datasets, the performance of the proposed framework is comparable with that of the top three systems.

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http://hdl.handle.net/10453/127667