Expert-guided contrastive opinion summarization for controversial issues

Jinlong Guo, Yujie Lu, Tatsunori Mori, Catherine Blake

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a new model for the task of contrastive opinion summarization (COS) particularly for controversial issues. Traditional COS methods, which mainly rely on sentence similarity measures are not sufficient for a complex controversial issue. We therefore propose an Expert-Guided Contrastive Opinion Summarization (ECOS) model. Compared to previous methods, our model can (1) integrate expert opinions with ordinary opinions from social media and (2) better align the contrastive arguments under the guidance of expert prior opinion. We create a new data set about a complex social issue with "sufficient" controversy and experimental results on this data show that the proposed model are effective for (1) producing better arguments summary in understanding a controversial issue and (2) generating contrastive sentence pairs.

Original languageEnglish (US)
Title of host publicationWWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web
PublisherAssociation for Computing Machinery
Pages1105-1110
Number of pages6
ISBN (Electronic)9781450334730
DOIs
StatePublished - May 18 2015
Event24th International Conference on World Wide Web, WWW 2015 - Florence, Italy
Duration: May 18 2015May 22 2015

Publication series

NameWWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web

Other

Other24th International Conference on World Wide Web, WWW 2015
Country/TerritoryItaly
CityFlorence
Period5/18/155/22/15

Keywords

  • Contrastive opinion summarization
  • Controversial issue
  • Opinion mining
  • Similarity
  • Topic model

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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