Exploiting Timelines to Enhance Multi-document Summarization
Research Area: Natural Language Processing Year: 2014
Type of Publication: In Proceedings  
  • Jun-Ping Ng
  • Yan Chen
  • Min-Yen Kan
  • Zhoujun Li
We study the use of temporal information in the form of timelines to enhance multidocument summarization. We employ a fully automated temporal processing system to generate a timeline for each input document. We derive three features from these timelines, and show that their use in supervised summarization lead to a significant 4.1% improvement in ROUGE performance over a state-of-the-art baseline. In addition, we propose TIMEMMR, a modification to Maximal Marginal Relevance that promotes temporal diversity by way of computing time span similarity, and show its utility in summarizing certain document sets. We also propose a filtering metric to discard noisy timelines generated by our automatic processes, to purify the timeline input for summarization. By selectively using timelines guided by filtering, overall summarization performance is increased by a significant 5.9%.
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