This paper presents a two-stage approach to summarizing multiple contrastive viewpoints in opinionated text. In the first stage, we use an unsupervised probabilistic approach to model and extract multiple viewpoints in text. We experiment with a variety of lexical and syntactic features, yielding significant performance gains over bag-of-words feature sets. In the second stage, we introduce Comparative LexRank, a novel random walk formulation to score sentences and pairs of sentences from opposite viewpoints based on both their representativeness of the collection as well as their contrastiveness with each other. Experimental results show that the proposed approach can generate informative summaries of viewpoints in opinionated text.

Original languageEnglish (US)
StatePublished - Oct 2010
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2010 - Cambridge, MA, United States
Duration: Oct 9 2010Oct 11 2010


OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2010
Country/TerritoryUnited States
CityCambridge, MA

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems


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