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	Graph-Based Multi-Modality Learning for Topic-Focused Multi-Document Summarization
	Wan, Xiaojun ; Xiao, Jianguo
	2009
英文摘要	Graph-based manifold-ranking methods have been successfully applied to topic-focused multi-document summarization. This paper further proposes to use the multi-modality manifold-ranking algorithm for extracting topic-focused summary from multiple documents by considering the within-document sentence relationships and the cross-document sentence relationships as two separate modalities (graphs). Three different fusion schemes, namely linear form, sequential form and score combination form, are exploited in the algorithm. Experimental results on the DUC benchmark datasets demonstrate the effectiveness of the proposed multi-modality learning algorithms with all the three fusion schemes.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000283727900253&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; EI; CPCI-S(ISTP); 12
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/321251]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Wan, Xiaojun,Xiao, Jianguo. Graph-Based Multi-Modality Learning for Topic-Focused Multi-Document Summarization. 2009-01-01.

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