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

Jen Yuan Yeh*, Hao Ren Ke, Wei Pang Yang

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

39 引文 斯高帕斯(Scopus)

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 an 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.

原文英語
頁(從 - 到)1451-1462
頁數12
期刊Expert Systems with Applications
35
發行號3
DOIs
出版狀態已發佈 - 2008 10月
對外發佈

ASJC Scopus subject areas

  • 工程 (全部)
  • 電腦科學應用
  • 人工智慧

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