iSpreadRank: Ranking sentences for extraction-based summarization using feature weight propagation in the sentence similarity network

作     者：Yeh, Jen-Yuan Ke, Hao-Ren Yang, Wei-Pang 

作者机构：Natl Chiao Tung Univ Dept Comp Sci Hsinchu 300 Taiwan Natl Chiao Tung Univ Inst Informat Management Hsinchu 300 Taiwan Natl Chiao Tung Univ Univ Lib Hsinchu 300 Taiwan Natl Dong Hwa Univ Dept Informat Management Hualien 974 Taiwan 

出 版 物：《EXPERT SYSTEMS WITH APPLICATIONS》 (专家系统及其应用)

年 卷 期：2008年第35卷第3期

页      面：1451-1462页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Council  NSC  (NSC-92-2213-E-009-126) 

主　　题：sentence extraction multidocument summarization spreading activation sentence similarity network feature weigh propagation social network analysis 

摘      要：Sentence extraction is a widely adopted text summarization technique where the most important sentences are extracted from document(s) and presented as a summary. The first step towards sentence extraction is to rank sentences in order of importance as in the summary. This paper proposes a novel graph-based ranking method, iSpreadRank, to perform this task. iSpreadRank models a set of topic-related documents into a sentence similarity network. Based on such a network model, iSpreadRank exploits the spreading activation theory to formulate a general concept from social network analysis: the importance of a node in a network (i.e., a sentence in this paper) is determined not only by the number of nodes to which it connects, but also by the importance of its connected nodes. The algorithm recursively re-weights the importance of sentences by spreading their sentence-specific feature scores throughout the network to adjust the importance of other sentences. Consequently, a ranking of sentences indicating the relative importance of sentences is reasoned. This paper also develops in approach to produce a generic extractive summary according to the inferred sentence ranking. The proposed summarization method is evaluated using the DUC 2004 data set, and found to perform well. Experimental results show that the proposed method obtains a ROUGE-1 score of 0.38068, which represents a slight difference of 0.00156, when compared with the best participant in the DUC 2004 evaluation. (C) 2007 Elsevier Ltd. All rights reserved.