Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77337
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorMa, S-
dc.creatorSun, X-
dc.creatorXu, J-
dc.creatorWang, H-
dc.creatorLi, W-
dc.creatorSu, Q-
dc.date.accessioned2018-07-30T08:27:39Z-
dc.date.available2018-07-30T08:27:39Z-
dc.identifier.isbn9781945626760-
dc.identifier.urihttp://hdl.handle.net/10397/77337-
dc.language.isoenen_US
dc.publisherAssociation for Computational Linguistics (ACL)en_US
dc.titleImproving semantic relevance for sequence-to-Sequence learning of Chinese social media text summarizationen_US
dc.typeConference Paperen_US
dc.identifier.spage635-
dc.identifier.epage640-
dc.identifier.volume2-
dc.identifier.doi10.18653/v1/P17-2100-
dcterms.abstractCurrent Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the summary representation is produced by a decoder. Besides, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.-
dcterms.bibliographicCitationACL 2017 - 55th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), 30 Jul - 4 Aug 2017, v. 2, p. 635-640-
dcterms.issued2017-
dc.identifier.scopus2-s2.0-85040635086-
dc.relation.conferenceAssociation for Computational Linguistics. Meeting [ACL]-
dc.description.validate201807 bcrc-
Appears in Collections:Conference Paper
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

SCOPUSTM   
Citations

22
Citations as of Aug 28, 2020

WEB OF SCIENCETM
Citations

9
Last Week
0
Last month
Citations as of Sep 21, 2020

Page view(s)

162
Citations as of Oct 18, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.