Timeline generation with social attention

Xin Wayne Zhao, Yanwei Guo, Rui Yan, Yulan He, Xiaoming Li

Research output: Chapter in Book/Published conference outputConference publication


Timeline generation is an important research task which can help users to have a quick understanding of the overall evolution of any given topic. It thus attracts much attention from research communities in recent years. Nevertheless, existing work on timeline generation often ignores an important factor, the attention attracted to topics of interest (hereafter termed "social attention"). Without taking into consideration social attention, the generated timelines may not reflect users' collective interests. In this paper, we study how to incorporate social attention in the generation of timeline summaries. In particular, for a given topic, we capture social attention by learning users' collective interests in the form of word distributions from Twitter, which are subsequently incorporated into a unified framework for timeline summary generation. We construct four evaluation sets over six diverse topics. We demonstrate that our proposed approach is able to generate both informative and interesting timelines. Our work sheds light on the feasibility of incorporating social attention into traditional text mining tasks.

Original languageEnglish
Title of host publicationSIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York, NY (US)
Number of pages4
ISBN (Print)9781450320344
Publication statusPublished - 28 Jul 2013
Event36th international ACM SIGIR conference - Dublin, Ireland
Duration: 28 Jul 20131 Aug 2013


Conference36th international ACM SIGIR conference
Abbreviated titleSIGIR 2013
OtherResearch and Development in Information Retrieval


  • social media attention
  • timeline
  • user interest


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