Journal
INFORMATION PROCESSING & MANAGEMENT
Volume 52, Issue 4, Pages 670-681Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.ipm.2015.12.012
Keywords
Multi-document summarization; Hypergraph-based ranking; HDP
Funding
- State Key Program of National Natural Science Foundation of China [61133012]
- National Natural Science Foundation of China [61373108]
- National Philosophy Social Science Major Bidding Project of China [11ZD189]
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General graph random walk has been successfully applied in multi-document summarization, but it has some limitations to process documents by this way. In this paper, we propose a novel hypergraph based vertex-reinforced random walk framework for multi document summarization. The framework first exploits the Hierarchical Dirichlet Process (HDP) topic model to learn a word-topic probability distribution in sentences. Then the hypergraph is used to capture both cluster relationship based on the word-topic probability distribution and pairwise similarity among sentences. Finally, a time-variant random walk algorithm for hypergraphs is developed to rank sentences which ensures sentence diversity by vertex-reinforcement in summaries. Experimental results on the public available dataset demonstrate the effectiveness of our framework. (C) 2015 Elsevier Ltd. All rights reserved.
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