CollabSum: exploiting multiple document clustering for collaborative single document summarizations

  • Authors:

  • Xiaojun Wan;Jianwu Yang

  • Affiliations:

  • Jianguo XiaoPeking University;Jianguo XiaoPeking University

  • Venue:
  • SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2007

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Abstract

Almost all existing methods conduct the summarization tasks for single documents separately without interactions for each document under the assumption that the documents are considered independent of each other. This paper proposes a novel framework called CollabSum for collaborative single document summarizations by making use of mutual influences of multiple documents within a cluster context. In this study, CollabSum is implemented by first employing the clustering algorithm to obtain appropriate document clusters and then exploiting the graph-ranking based algorithm for collaborative document summarizations within each cluster. Both the with-document and cross-document relationships between sentences are incorporated in the algorithm. Experiments on the DUC2001 and DUC2002 datasets demonstrate the encouraging performance of the proposed approach. Different clustering algorithms have been investigated and we find that the summarization performance relies positively on the quality of document cluster.