Timeline generation with social attention

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

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)
PublisherACM
Pages1061-1064
Number of pages4
ISBN (Print)9781450320344
DOIs
Publication statusPublished - 28 Jul 2013
Event36th international ACM SIGIR conference - Dublin, Ireland
Duration: 28 Jul 20131 Aug 2013

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

Cite this

Zhao, X. W., Guo, Y., Yan, R., He, Y., & Li, X. (2013). Timeline generation with social attention. In SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1061-1064). New York, NY (US): ACM. https://doi.org/10.1145/2484028.2484103
Zhao, Xin Wayne ; Guo, Yanwei ; Yan, Rui ; He, Yulan ; Li, Xiaoming. / Timeline generation with social attention. SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY (US) : ACM, 2013. pp. 1061-1064
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Zhao, XW, Guo, Y, Yan, R, He, Y & Li, X 2013, Timeline generation with social attention. in SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, New York, NY (US), pp. 1061-1064, 36th international ACM SIGIR conference, Dublin, Ireland, 28/07/13. https://doi.org/10.1145/2484028.2484103

Timeline generation with social attention. / Zhao, Xin Wayne; Guo, Yanwei; Yan, Rui; He, Yulan; Li, Xiaoming.

SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY (US) : ACM, 2013. p. 1061-1064.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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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Zhao XW, Guo Y, Yan R, He Y, Li X. Timeline generation with social attention. In SIGIR 2013 - Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY (US): ACM. 2013. p. 1061-1064 https://doi.org/10.1145/2484028.2484103