Combining co-clustering with noise detection for theme-based summarization

Xiaoyan Cai, Wenjie Li, Renxian Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


To overcome the fact that the length of sentences is short and their content is limited, we regard words as independent text objects rather than features of sentences in sentence clustering and develop two coclustering frameworks, namely integrated clustering and interactive clustering, to cluster sentences and words simultaneously. Since real-world datasets always contain noise, we incorporate noise detection and removal to enhance clustering of sentences and words. Meanwhile, a semisupervised approach is explored to incorporate the query information (and the sentence information in early document sets) in themebased summarization. Thorough experimental studies are conducted. When evaluated on the DUC2005-2007 datasets and TAC 2008-2009 datasets, the performance of the two noise-detecting co-clustering approaches is comparable with that of the top three systems. The results also demonstrate that the interactive with noise detection algorithm is more effective than the noise-detecting integrated algorithm.
Original languageEnglish
Article number16
JournalACM Transactions on Speech and Language Processing
Issue number4
Publication statusPublished - 1 Dec 2013


  • Document analysis
  • Noise detection
  • Sentence and word co-clustering
  • Theme-based summarization

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computational Mathematics


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