Paper: Helpfulness-Guided Review Summarization

ACL ID N13-2011
Title Helpfulness-Guided Review Summarization
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Student Session
Year 2013
Authors

Review mining and summarization has been a hot topic for the past decade. A lot of ef- fort has been devoted to aspect detection and sentiment analysis under the assumption that every review has the same utility for related tasks. However, reviews are not equally help- ful as indicated by user-provided helpfulness assessment associated with the reviews. In this thesis, we propose a novel review sum- marization framework which summarizes re- view content under the supervision of auto- mated assessment of review helpfulness. This helpfulness-guided framework can be easily adapted to traditional review summarization tasks, for a wide range of domains.