Compact explanatory opinion summarization

Hyun Duk Kim, Malu Castellanos, Meichun Hsu, Cheng Xiang Zhai, Umeshwar Dayal, Riddhiman Ghosh

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

Abstract

In this paper, we propose a novel opinion summarization problem called compact explanatory opinion summarization (CEOS) which aims to extract within-sentence explanatory text segments from input opinionated texts to help users better understand the detailed reasons of sentiments. We propose and study general methods for identifying candidate boundaries and scoring the explanatoriness of text segments using Hidden Markov Models. We create new data sets and use a new evaluation measure to evaluate CEOS. Experimental results show that the proposed methods are effective for generating an explanatory opinion summary, outperforming a standard text summarization method. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages1697-1702
Number of pages6
DOIs
StatePublished - 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

Keywords

  • Compact explanatory summarization
  • Explanatory phrase extraction
  • Opinion mining

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting

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