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Title:
Update Summarization via Graph-Based Sentence Ranking
Author: Li, Xuan ; Du, Liang ; Shen, Yi-Dong
Keyword: Summarization ; update summarization ; topic-focused summarization ; multidocument summarization ; extraction-based summarization ; graph-based ranking ; manifold ranking ; large-margin constrained ranking ; novelty ; quadratically constrained quadratic programming ; quadratic programming
Source: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Issued Date: 2013
Volume: 25, Issue:5, Pages:1162-1174
Indexed Type: SCI
Department: [Li, Xuan; Du, Liang; Shen, Yi-Dong] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China.
Abstract: Due to the fast evolution of the information on the Internet, update summarization has received much attention in recent years. It is to summarize an evolutionary document collection at current time supposing the users have read some related previous documents. In this paper, we propose a graph-ranking-based method. It performs constrained reinforcements on a sentence graph, which unifies previous and current documents, to determine the salience of the sentences. The constraints ensure that the most salient sentences in current documents are updates to previous documents. Since this method is NP-hard, we then propose its approximate method, which is polynomial time solvable. Experiments on the TAC 2008 and 2009 benchmark data sets show the effectiveness and efficiency of our method.
English Abstract: Due to the fast evolution of the information on the Internet, update summarization has received much attention in recent years. It is to summarize an evolutionary document collection at current time supposing the users have read some related previous documents. In this paper, we propose a graph-ranking-based method. It performs constrained reinforcements on a sentence graph, which unifies previous and current documents, to determine the salience of the sentences. The constraints ensure that the most salient sentences in current documents are updates to previous documents. Since this method is NP-hard, we then propose its approximate method, which is polynomial time solvable. Experiments on the TAC 2008 and 2009 benchmark data sets show the effectiveness and efficiency of our method.
Language: 英语
WOS ID: WOS:000316755100016
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/16936
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Li, Xuan,Du, Liang,Shen, Yi-Dong. Update Summarization via Graph-Based Sentence Ranking[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2013-01-01,25(5):1162-1174.
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