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Volume 29 Issue 3
May  2014
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Po Hu, Min-Lie Huang, Xiao-Yan Zhu. Exploring the Interactions of Storylines from Informative News Events[J]. Journal of Computer Science and Technology, 2014, 29(3): 502-518. DOI: 10.1007/s11390-014-1445-6
Citation: Po Hu, Min-Lie Huang, Xiao-Yan Zhu. Exploring the Interactions of Storylines from Informative News Events[J]. Journal of Computer Science and Technology, 2014, 29(3): 502-518. DOI: 10.1007/s11390-014-1445-6
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Exploring the Interactions of Storylines from Informative News Events

  • State Key Laboratory of Intelligent Technology and Systems, Department of Computer Science and Technology Tsinghua University, Beijing 100084, China

Funds: Supported by the National Basic Research 973 Program of China under Grant No. 2012CB316301, the National Natural Science Foundation of China under Grant No. 60803075, the Tsinghua University Initiative Scientific Research Program under Grant No. 20121088071, and the Beijing Higher Education Young Elite Teacher Project.
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  • Author Bio:

    Po Hu is a fifth year Ph.D. student in the Department of Computer Science and Technology, Tsinghua University, Beijing. He received his bachelor degree from the Department of Computer Science and Technology, Tsinghua University, in 2009. Po has published and coauthored several research papers in ICDM, CIKM, and SIGKDD conferences. His research interest includes textual temporal analysis, document retrieval, and topic modeling.

  • Received Date: August 31, 2013
  • Revised Date: March 04, 2014
  • Published Date: May 04, 2014
    Abstract
  • Today's news readers can be easily overwhelmed by the numerous news articles online. To cope with information overload, online news media publishes timelines for continuously developing news topics. However, the timeline summary does not show the relationship of storylines, and is not intuitive for readers to comprehend the development of a complex news topic. In this paper, we study a novel problem of exploring the interactions of storylines in a news topic. An interaction of two storylines is signified by informative news events that play a key role in both storylines. Storyline interactions can indicate key phases of a news topic, and reveal the latent connections among various aspects of the story. We address the coherence between news articles which is not considered in traditional similarity-based methods, and discover salient storyline interactions to form a clear, global picture of the news topic. User preference can be naturally integrated into our method to generate query-specific results. Comprehensive experiments on ten news topics show the effectiveness of our method over alternative approaches.
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