PNR2: Ranking sentences with positive and negative reinforcement for query-oriented update summarization

Wenjie Li, Furu Wei, Qin Lu, Yanxiang He

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

35 Citations (Scopus)


Query-oriented update summarization is an emerging summarization task very recently. It brings new challenges to the sentence ranking algorithms that require not only to locate the important and query-relevant information, but also to capture the new information when document collections evolve. In this paper, we propose a novel graph based sentence ranking algorithm, namely PNR2, for update summarization. Inspired by the intuition that "a sentence receives a positive influence from the sentences that correlate to it in the same collection, whereas a sentence receives a negative influence from the sentences that correlates to it in the different (perhaps previously read) collection", PNR2 models both the positive and the negative mutual reinforcement in the ranking process. Automatic evaluation on the DUC 2007 data set pilot task demonstrates the effectiveness of the algorithm. Licensed under the Creative Commons.
Original languageEnglish
Title of host publicationColing 2008 - 22nd International Conference on Computational Linguistics, Proceedings of the Conference
Number of pages8
Publication statusPublished - 1 Dec 2008
Event22nd International Conference on Computational Linguistics, Coling 2008 - Manchester, United Kingdom
Duration: 18 Aug 200822 Aug 2008


Conference22nd International Conference on Computational Linguistics, Coling 2008
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

Cite this