A cluster-sensitive graph model for query-oriented multi-document summarization

Furu Wei, Wenjie Li, Qin Lu, Yanxiang He

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


In this paper, we develop a novel cluster-sensitive graph model for query-oriented multi-document summarization. Upon it, an iterative algorithm, namely QoCsR, is built. As there is existence of natural clusters in the graph in the case that a document comprises a collection of sentences, we suggest distinguishing intra- and inter-document sentence relations in order to take into consideration the influence of cluster (i.e. document) global information on local sentence evaluation. In our model, five kinds of relations are involved among the three objects, i.e. document, sentence and query. Three of them are new and normally ignored in previous graph-based models. All these relations are then appropriately formulated in the QoCsR algorithm though in different ways. ROUGE evaluations shows that QoCsR can outperform the best DUC 2005 participating systems.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 30th European Conference on IR Research, ECIR 2008, Proceedings
Number of pages8
Publication statusPublished - 14 Apr 2008
Event30th Annual European Conference on Information Retrieval, ECIR 2008 - Glasgow, United Kingdom
Duration: 30 Mar 20083 Apr 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4956 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th Annual European Conference on Information Retrieval, ECIR 2008
Country/TerritoryUnited Kingdom


  • Graph model and ranking algorithm
  • Multi-document summarization
  • Query-oriented summarization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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