Timeline generation with social attention

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Abstract

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.

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Details

Publication date28 Jul 2013
Publication titleSIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York, NY (US)
PublisherACM
Pages1061-1064
Number of pages4
ISBN (Print)9781450320344
Original languageEnglish
Event36th international ACM SIGIR conference - Dublin, Ireland

Conference

Conference36th international ACM SIGIR conference
Abbreviated titleSIGIR 2013
CountryIreland
CityDublin
Period28/07/131/08/13
OtherResearch and Development in Information Retrieval

    Keywords

  • social media attention, timeline, user interest

DOI

Employable Graduates; Exploitable Research

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