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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1451-1462
Number of pages12
JournalExpert Systems with Applications
Issue number3
Publication statusPublished - 2008 Oct
Externally publishedYes


  • Feature weigh propagation
  • Multidocument summarization
  • Sentence extraction
  • Sentence similarity network
  • Social network analysis
  • Spreading activation

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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