Paper: Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews

ACL ID C14-1187
Title Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

We propose a novel unsupervised extractive approach for summarizing online reviews by ex- ploiting review helpfulness ratings. In addition to using the helpfulness ratings for review-level filtering, we suggest using them as the supervision of a topic model for sentence-level content scoring. The proposed method is metadata-driven, requiring no human annotation, and generaliz- able to different kinds of online reviews. Our experiment based on a widely used multi-document summarization framework shows that our helpfulness-guided review summarizers significantly outperform a traditional content-based summarizer in both human evaluation and automated eval- uation.