Compact explanatory opinion summarization

  • Authors:

  • Hyun Duk Kim;Malu Castellanos;Meichun Hsu;ChengXiang Zhai;Umeshwar Dayal;Riddhiman Ghosh

  • Affiliations:

  • University of Illinois at Urbana-Champaign, Urbana, IL, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Palo Alto, CA, USA;University of Illinois at Urbana-Champaign, Urbana, IL, USA;HP Laboratories, Palo Alto, CA, USA;HP Laboratories, Palo Alto, CA, USA

  • Venue:
  • Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
  • Year:
  • 2013

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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.