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Frontiers of Computer Science

, Volume 10, Issue 4, pp 702–716 | Cite as

Generating timeline summaries with social media attention

  • Wayne Xin ZhaoEmail author
  • Ji-Rong Wen
  • Xiaoming Li
Research Article
  • 120 Downloads

Abstract

Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of one given topic. Previous methods simply split the time span into fixed, equal time intervals without studying the role of the evolutionary patterns of the underlying topic in timeline generation. In addition, few of these methods take users’ collective interests into considerations to generate timelines.

We consider utilizing social media attention to address these two problems due to the facts: 1) social media is an important pool of real users’ collective interests; 2) the information cascades generated in it might be good indicators for boundaries of topic phases. Employing Twitter as a basis, we propose to incorporate topic phases and user’s collective interests which are learnt from social media into a unified timeline generation algorithm.We construct both one informativeness-oriented and three interestingness-oriented evaluation sets over five topics.We demonstrate that it is very effective to generate both informative and interesting timelines. In addition, our idea naturally leads to a novel presentation of timelines, i.e., phase based timelines, which can potentially improve user experience.

Keywords

timeline social media attention phase users’ collective interests 

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Supplementary material

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.Beijing Key Laboratory of Big Data Management and Analysis MethodsRenmin University of ChinaBeijingChina
  3. 3.School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina
  4. 4.Beijing Key Laboratory on Integration and Analysis of Large-scale Stream DataBeijingChina

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