Paper: Summarizing Contrastive Viewpoints in Opinionated Text

ACL ID D10-1007
Title Summarizing Contrastive Viewpoints in Opinionated Text
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2010

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 perfor- mance gains over bag-of-words feature sets. In the second stage, we introduce Compara- tive LexRank, a novel random walk formula- tion 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. Exper- imental results show that the proposed ap- proach can generate informative summaries of viewpoints in opinionated text.