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Timeline Summarization from Relevant Headlines

  • Giang Tran
  • Mohammad Alrifai
  • Eelco Herder
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9022)

Abstract

Timeline summaries are an effective way for helping newspaper readers to keep track of long-lasting news stories, such as the Egypt revolution. A good timeline summary provides a concise description of only the main events, while maintaining good understandability. As manual construction of timelines is very time-consuming, there is a need for automatic approaches. However, automatic selection of relevant events is challenging due to the large amount of news articles published every day. Furthermore, current state-of-the-art systems produce summaries that are suboptimal in terms of relevance and understandability. We present a new approach that exploits the headlines of online news articles instead of the articles’ full text. The quantitative and qualitative results from our user studies confirm that our method outperforms state-of-the-art system in these aspects.

Keywords

News Article News Story Random Walk Model News Agency Word Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Giang Tran
    • 1
  • Mohammad Alrifai
    • 1
  • Eelco Herder
    • 1
  1. 1.L3S Research Center and Leibniz University HannoverHannoverGermany

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