An Effective Joint Framework for Document Summarization

Gui, M; Zhang, Z; Yang, Z; Gu, Y; Xu, G

An Effective Joint Framework for Document Summarization

Gui, M Zhang, Z Yang, Z Gu, Y Xu, G

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Publisher:: ACM Press
Publication Type:: Conference Proceeding
Citation:: The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018, 2018, pp. 121-122
Issue Date:: 2018-04-23

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Field	Value	Language
dc.contributor.author	Gui, M
dc.contributor.author	Zhang, Z
dc.contributor.author	Yang, Z
dc.contributor.author	Gu, Y
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date	2018-04-23
dc.date.accessioned	2020-09-30T11:29:51Z
dc.date.available	2020-09-30T11:29:51Z
dc.date.issued	2018-04-23
dc.identifier.citation	The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018, 2018, pp. 121-122
dc.identifier.isbn	9781450356404
dc.identifier.uri	http://hdl.handle.net/10453/142940
dc.description.abstract	© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License. Document summarization is an important research issue and has attracted much attention from the academe. The approaches for document summarization can be classified as extractive and abstractive. In this work, we introduce an effective joint framework that integrates extractive and abstractive summarization models, which is much closer to the way human write summaries (first underlining important information). Preliminary experiments on real benchmark dataset demonstrate that our model is competitive with the state-of-the-art methods.
dc.language	en
dc.publisher	ACM Press
dc.relation.ispartof	The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018
dc.relation.ispartof	Companion of the The Web Conference 2018
dc.relation.isbasedon	10.1145/3184558.3186959
dc.rights	info:eu-repo/semantics/openAccess
dc.title	An Effective Joint Framework for Document Summarization
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2020-09-30T11:29:43Z
pubs.finish-date	2018-04-27
pubs.publication-status	Published
pubs.start-date	2018-04-23

Abstract:

© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License. Document summarization is an important research issue and has attracted much attention from the academe. The approaches for document summarization can be classified as extractive and abstractive. In this work, we introduce an effective joint framework that integrates extractive and abstractive summarization models, which is much closer to the way human write summaries (first underlining important information). Preliminary experiments on real benchmark dataset demonstrate that our model is competitive with the state-of-the-art methods.

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http://hdl.handle.net/10453/142940