Opinosis: A graph-based approach to abstractive summarization of highly redundant opinions

Kavita Ganesan, Cheng Xiang Zhai, Jiawei Han

Research output: Contribution to conferencePaper

Abstract

We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.

Original languageEnglish (US)
Pages340-348
Number of pages9
StatePublished - Dec 1 2010
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: Aug 23 2010Aug 27 2010

Other

Other23rd International Conference on Computational Linguistics, Coling 2010
CountryChina
CityBeijing
Period8/23/108/27/10

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

Fingerprint Dive into the research topics of 'Opinosis: A graph-based approach to abstractive summarization of highly redundant opinions'. Together they form a unique fingerprint.

  • Cite this

    Ganesan, K., Zhai, C. X., & Han, J. (2010). Opinosis: A graph-based approach to abstractive summarization of highly redundant opinions. 340-348. Paper presented at 23rd International Conference on Computational Linguistics, Coling 2010, Beijing, China.