Paper: Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

ACL ID C10-1039
Title Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

ve Summarization of Highly Redundant Opinions Kavita Ganesan and ChengXiang Zhai and Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign {kganes2,czhai,hanj}@cs.uiuc.edu Abstract We present a novel graph-based summa- rization framework (Opinosis) that generates concise abstractive summaries of highly re- dundant opinions. Evaluation results on sum- marizing user reviews show that Opinosis summaries have better agreement with hu- man summaries compared to the baseline ex- tractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.