Document (#39973)

Author
Xiong, S.
Ji, D.
Title
Query-focused multi-document summarization using hypergraph-based ranking
Source
Information processing and management. 52(2016) no.4, S.670-681
Year
2016
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.
Content
Vgl.: http://www.sciencedirect.com/science/article/pii/S0306457315001491.
Theme
Automatisches Abstracting

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  3. Pandey, S.; Khanna, P.; Yokota, H.: ¬A semantics and image retrieval system for hierarchical image databases (2016) 3.45
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  5. Wei, F.; Li, W.; Lu, Q.; He, Y.: Applying two-level reinforcement ranking in query-oriented multidocument summarization (2009) 0.26
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