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Improving Diversity of Focused Summaries through the Negative Endorsements of Redundant Facts
Conference proceeding   Open access

Improving Diversity of Focused Summaries through the Negative Endorsements of Redundant Facts

Palakorn Achananuparp, Xiaohua Hu, Lifan Guo, Tingting He, Yuan An and Zhoujun Li
Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, v 1, pp 342-349
31 Aug 2010
DOI: https://doi.org/10.1109/WI-IAT.2010.36
url
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.617.7576View
SubmittedOpen Access (License Unspecified),  Open

Abstract

focused summarization, diversity, random walks, sentence graph, negative edges
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.

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