Extracting Shared Topics of Multiple Documents (original) (raw)

Abstract

In this paper, we present a weighted graph based method to simultaneously compare the textual content of two or more documents and extract the shared (sub)topics of them, if available. A set of documents are modelled with a set of pairwise weighted bipartite graphs. A generalized mutual reinforcement principle is applied to the pairwise bipartite graphs to calculate the saliency scores of sentences in each documents based on pairwise weighted bipartite graphs. Sentences with advantaged saliency are selected, and they together convey the dominant shared topic. If there are more than one shared subtopics among the documents, a spectral min-max cut algorithm can be used to partition a derived sentence similarity graph into several subgraphs. For a subgraph, if all documents contribute some sentences(nodes) to it, then these sentences(nodes) in the subgraph may convey a shared subtopic. The generalized mutual reinforcement principle is applied to them to verify and extract the shared subtopic.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, The Pennsylvania State University, University Park, PA, 16802, USA
    Xiang Ji & Hongyuan Zha

Authors

  1. Xiang Ji
  2. Hongyuan Zha

Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea
    Kyu-Young Whang
  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea
    Jongwoo Jeon
  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea
    Kyuseok Shim
  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA
    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Ji, X., Zha, H. (2003). Extracting Shared Topics of Multiple Documents. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8\_10

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