Generating timeline summaries with social media attention

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

References

[1] Swan R, Allan J. Automatic generation of overview timelines. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2000, 49–56

[2] Chieu H L, Lee Y K. Query based event extraction along a timeline. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2004, 425–432

[3] Yan R, Wan X J, Otterbacher J, Kong L, Li X M, Zhang Y. Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In: Proceedings of the 34th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval. 2011, 745–754

[4] Yan R, Kong L, Huang C R, Wan X J, Li X M, Zhang Y. Timeline generation through evolutionary trans-temporal summarization. In: Proceedings of the Conference on EmpiricalMethods in Natural Language Processing. 2011, 433–443

[5] Yan R, Nie J Y, Li X M. Summarize what you are interested in: an optimization framework for interactive personalized summarization. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 1342–1351

[6] Kleinberg J. Bursty and hierarchical structure in streams. Data Mining Knowledge Discovery, 2003, 7(4): 373–397

[7] Wu S M, Hofman J M, Maso n W A, Watts J D. Who says what to whom on twitter. In: Proceedings of the 20th International World Wide Web Conference. 2011, 705–714

[8] Zhai C X, Lafferty J. Model-based feedback in the language modeling approach to information retrieval. In: Proceedings of the 10th ACM International Conference on Information and Knowledge Management. 2001, 403–410

[9] Erkan G, Radev D R. LexPageRank: prestige in multi-document text summarization. In: Proceedings of the Conference on Empirical Methods on Natural Language Processing. 2004, 365–371

[10] Wan X J, Yang J W, Xiao J G. Manifold-ranking based topic-focused multi-document summarization. In: Proceedings of the International Joint Conference on Artificial Intelligence. 2007, 2903–2908

[11] Mei Q Z, Guo J, Radev , D. DivRank: the interplay of prestige and diversity in information networks. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2010, 1009–1018

[12] Yang J, Leskovec J. Patterns of temporal variation in online media. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining. 2011, 177–186

[13] Leskovec J, Backstrom L, Kleinberg , J. Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD Conference on Knowledge Discovery and DataMining. 2009, 497–506

[14] Radev D R, Jing H Y, Sty M, Tam D. Centroid-based summarization of multiple documents. Information Processing and Management, 2004, 40(6): 919–938

[15] Wan X J, Yang J W. Multi-document summarization using clusterbased link analysis. In: Proceedings of the 31st ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 299–306

[16] Zhao X W, Shu B H, Jiang J, Song Y, Yan H F, Li X M. Identifying event-related bursts via social media activities. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 2012, 1466–1477

[17] Lin C Y, Hovy E. From single to multi-document summarization: a prototype system and its evaluation. In: Proceedings of the 40th Annual Conference of the Association for Computational Linguistics. 2002, 457–464

[18] Wan X J, Yang J W, Xiao J G. Single document summarization with document expansion. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2007, 931–936

[19] Li L D, Zhou K, Xue G R, Zha H Y, Yu Y. Enhancing diversity, coverage and balance for summarization through structure learning. In: Proceedings of the 18th International World Wide Web Conference. 2009, 71–80

[20] Goldstein J, Kantrowitz M, Mittal V, Carbonell J. Summarizing text documents: sentence selection and evaluation metrics. In: Proceedings of the 22nd ACM SIGIR Conference on Research and Development in Information Retrieval. 1999, 121–128

[21] Leuski A, Lin C Y, Hovy E. iNeATS: interactive multi-document summarization. In: Proceedings of the 41st Conference of the Association for Computational Linguistics. 2003, 125–128

[22] Allan J, Gupta R, Khandelwal V. Temporal summaries of new topics. In: Proceedings of the 24th ACM SIGIR Conference on Research and Development in Information Retrieval. 2001, 10–18

[23] Yang Z, Cai K K, Tang J, Zhang L, Su Z, Li J Z. Social context summarization. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 255–264

[24] Swan R, Allan J. Automatic generation of overview timelines. In: Proceedings of the 23rd Annual ACM SIGIR Conference on Research and Development in Information Retrieval. 2000, 49–56

[25] Fung G P C, Yu J X, Yu P S, Lu H J. Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases. 2005, 181–192

[26] Mathioudakis M, Koudas N. TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM International Conference on Management of Data. 2010, 1155–1158

[27] Zubiaga A, Spina D, Fresno V, Martínez R. Classifying trending topics: a typology of conversation triggers on Twitter. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management. 2011, 2461–2464

[28] Budak C, Agrawal D, Abbadi A E. Structural trend analysis for online social networks. The Proceedings of the VLDB Endowment, 2011, 4(10): 646–656

[29] Naaman M, Becker H, Gravano L. Hip and trendy: characterizing emerging Trends on twitter. Journal of American Society for Information Science and Techonology, 2011, 62(5): 902–918

[30] Sakaki T, Okazaki M, Matsuo Y. Earthquake shakes Twitter users: realtime event detection by social sensors. In: Proceedings of the 19th International World Wide Web Conference. 2010, 851–860

[31] Aramki E, Maskawa S, Morita M. Twitter catches the flu: Detecting influenza epidemics using Twitter. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing. 2011, 1466–1477

[32] Marcus A, Bernstein M S, Badar O, Karger D R, Madden S, Miller R C. Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of ACM Conference on Human Factors in Computing Systems. 2011, 227–236

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