Query-focused multi-document summarization using hypergraph-based ranking. Issue 4 (July 2016)

• Record Type: Journal Article
• Authors: Xiong, Shufeng; Ji, Donghong
• Is Part Of: Information processing & management. Volume 52:Issue 4(2016:Jul.)
• Page Start: 670
• Page End: 681
• Publication Date: 2016-07
• Subjects: Multi-document summarization -- Hypergraph-based ranking -- HDP; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2015.12.012
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: We propose a novel hybrid method to capture group relation of sentences. We cluster sentences with a KL-divergence based on word-topic distribution. We proposed a vertex reinforcement random walk process in a hypergraph model. The process simultaneously consider the query similarity, the centrality and the diversity of sentences. We implement our framework and verify improvement over appropriate baselines. 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.