NDLI: Ranking Through Clustering: An Integrated Approach to Multi-Document Summarization

Content Provider	IEEE Xplore Digital Library
Author	Xiaoyan Cai Wenjie Li
Copyright Year	2006
Abstract	Multi-document summarization aims to create a condensed summary while retaining the main characteristics of the original set of documents. Under such background, sentence ranking has hitherto been the issue of most concern. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering has been explored in the literature in order to provide more informative summaries. For each topic theme, the rank of terms conditional on this topic theme should be very distinct, and quite different from the rank of terms in other topic themes. Existing cluster-based summarization approaches apply clustering and ranking in isolation, which leads to incomplete, or sometimes rather biased, analytical results. A newly emerged framework uses sentence clustering results to improve or refine the sentence ranking results. Under this framework, we propose a novel approach that directly generates clusters integrated with ranking in this paper. The basic idea of the approach is that ranking distribution of sentences in each cluster should be quite different from each other, which may serve as features of clusters and new clustering measures of sentences can be calculated accordingly. Meanwhile, better clustering results can achieve better ranking results. As a result, ranking and clustering by mutually and simultaneously updating each other so that the performance of both can be improved. The effectiveness of the proposed approach is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004-2007 datasets.
Sponsorship	IEEE Signal Processing Society
Starting Page	1424
Ending Page	1433
Page Count	10
File Size	2048779
File Format	PDF
ISSN	15587916
Volume Number	21
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Equations Mathematical model Clustering algorithms Semantics Hidden Markov models Estimation Vectors sentence ranking Document summarization sentence clustering
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering

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