Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33912
Title: A document-sensitive graph model for multi-document summarization
Authors: Wei, F
Li, W 
Lu, Q 
He, Y
Keywords: Generic summarization
Graph-based ranking algorithm
Graph-based summarization model
Inter- and intra-document relation
Query-oriented summarization
Issue Date: 2010
Publisher: Springer
Source: Knowledge and information systems, 2010, v. 22, no. 2, p. 245-259 How to cite?
Journal: Knowledge and information systems 
Abstract: In recent years, graph-based models and ranking algorithms have drawn considerable attention from the extractive document summarization community. Most existing approaches take into account sentence-level relations (e.g. sentence similarity) but neglect the difference among documents and the influence of documents on sentences. In this paper, we present a novel document-sensitive graph model that emphasizes the influence of global document set information on local sentence evaluation. By exploiting document-document and document-sentence relations, we distinguish intra-document sentence relations from inter-document sentence relations. In such a way, we move towards the goal of truly summarizing multiple documents rather than a single combined document. Based on this model, we develop an iterative sentence ranking algorithm, namely DsR (Document-Sensitive Ranking). Automatic ROUGE evaluations on the DUC data sets show that DsR outperforms previous graph-based models in both generic and query-oriented summarization tasks.
URI: http://hdl.handle.net/10397/33912
ISSN: 0219-1377
EISSN: 0219-3116
DOI: 10.1007/s10115-009-0194-2
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