Multi-factors based sentence ordering for cross-document fusion from multimodal content

Yue, L; Shi, Z; Han, J; Wang, S; Chen, W; Zuo, W

Multi-factors based sentence ordering for cross-document fusion from multimodal content

Yue, L Shi, Z Han, J Wang, S Chen, W Zuo, W

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2017, 253 pp. 6 - 14
Issue Date:: 2017-08-30

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Field	Value	Language
dc.contributor.author	Yue, L	en_US
dc.contributor.author	Shi, Z	en_US
dc.contributor.author	Han, J	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Chen, W	en_US
dc.contributor.author	Zuo, W	en_US
dc.date.issued	2017-08-30	en_US
dc.identifier.citation	Neurocomputing, 2017, 253 pp. 6 - 14	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/124829
dc.description.abstract	© 2017 Organizing a coherent structure of the sentences extracted from multiple documents, guarantees the fluency and readability of the fused document. In this paper, sentence ordering problem is treated as a combinatorial optimization problem and solved with continuous Hopfield neural network (CHNN). We unify the existing factors by considering the most frequent orders temporal information, and topical relevance between local themes during overall ordering process. Specifically, ordering algorithm traverses all the local themes and locates a shortest path as the final sentence ordering. We show the results with data from Document Understanding Conferences (DUC) 2002–2005, and demonstrate the effectiveness of the developed approach compared with Random Ordering (RO), Chronological Ordering (CO), Majority Ordering (MO), and Precedence Relation Ordering (PRO).	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2016.12.084	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Multi-factors based sentence ordering for cross-document fusion from multimodal content	en_US
dc.type	Journal Article
utslib.citation.volume	253	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and 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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	253	en_US

Abstract:

© 2017 Organizing a coherent structure of the sentences extracted from multiple documents, guarantees the fluency and readability of the fused document. In this paper, sentence ordering problem is treated as a combinatorial optimization problem and solved with continuous Hopfield neural network (CHNN). We unify the existing factors by considering the most frequent orders temporal information, and topical relevance between local themes during overall ordering process. Specifically, ordering algorithm traverses all the local themes and locates a shortest path as the final sentence ordering. We show the results with data from Document Understanding Conferences (DUC) 2002–2005, and demonstrate the effectiveness of the developed approach compared with Random Ordering (RO), Chronological Ordering (CO), Majority Ordering (MO), and Precedence Relation Ordering (PRO).

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http://hdl.handle.net/10453/124829