Query-focused multi-document summarization using hypergraph-based ranking |
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| Institution: | 1. Computer School, Wuhan University, 430072 Wuhan, China;2. PingDingShan University, 467099 PingDingShan, China;1. Qatar Computing Research Institute, HBKU, Doha, Qatar;2. Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar;1. College of Education Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China;2. College of Business and Administration, Zhejiang University of Technology, Hangzhou, 310023, China;3. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, 410082, China |
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| Abstract: | 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. |
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