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Improving diversity of focused summaries through the negative endorsements of redundant facts

  • Palakorn Achananuparp*
  • , Xiaohua Hu
  • , Lifan Guo
  • , Tingting He
  • , Yuan An
  • , Zhoujun Li
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We present NegativeRank, a novel graph-based sentence ranking model to improve the diversity of focused summary by performing random walks over sentence graph with negative edge weights. Unlike the typical eigenvector centrality ranking, our method models the redundancy among sentence nodes as the negative edges. The negative edges can be thought of as the propagation of disapproval votes which can be used to penalize redundant sentences. As the iterative process continues, the initial ranking score of a given node will be adjusted according to a long-term negative endorsement from other sentence nodes. The evaluation results confirm that our proposed method is very effective in improving the diversity of the focused summary, compared to several well-known text summarization methods.

Original languageEnglish
Title of host publication2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Pages342-349
Number of pages8
DOIs
StatePublished - 2010
Event2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010 - Toronto, ON, Canada
Duration: 31 Aug 20103 Sep 2010

Publication series

NameProceedings - 2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Volume1

Conference

Conference2010 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2010
Country/TerritoryCanada
CityToronto, ON
Period31/08/103/09/10

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