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SCIENCE CHINA Information Sciences, Volume 61, Issue 12: 129101(2018) https://doi.org/10.1007/s11432-016-9333-4

An event summarizing algorithm based on the timeline relevance model in Sina Weibo

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  • ReceivedSep 16, 2017
  • AcceptedJan 8, 2018
  • PublishedJun 19, 2018

Abstract

There is no abstract available for this article.


Acknowledgment

This work was supported by Shenzhen Key Fundamental Research Projects (Grant Nos. JCYJ20170412150946024, JCYJ2017041 2151008290).


References

[1] Yih W T, Goodman J, Vanderwende L, et al. Multi-document summarization by maximizing informative content-words. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, 2007. 1776--1782. Google Scholar

[2] Ferreira R, Cabral L D S, Freitas F. A multi-document summarization system based on statistics and linguistic treatment. Expert Syst Appl, 2014, 41: 5780-5787 CrossRef Google Scholar

[3] Li J X, Li L, Li T. Mssf: a multi-document summarization framework based on submodularity. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, Beijing, 2011. 1247--1248. Google Scholar

[4] Wang D D, Li T, Zhu S H, et al. Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, 2008. 307--314. Google Scholar

[5] Mihalcea R, Tarau P. TextRank: bringing order into texts. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, 2004. 404--411. Google Scholar

[6] Yu Q, Weng W T, Zhang K, et al. Hot topic analysis and content mining in social media. In: Proceedings of the 33rd IEEE International Performance Computing and Communications Conference (IPCCC), Austin, 2014. Google Scholar

[7] Chawla S, Bedi P. Query expansion using information scent. In: Proceedings of International Symposium on Information Technology, Kuala Lumpur, 2008. Google Scholar

  • Table 1   The hourly gap between the searching and tweeting activities during different stages
    Different stages of the lifecycle Hourly gap (h) Proportion (%)
    $|t_{\text{start-search}}$(Growth period) $-$ $t_{\text{start-tweet}}$(Birth period)$|$ 12–24 76.31
    $|t_{\text{end-search}}$(Growth period) $-$ $t_{\text{end-tweet}}$(Birth period)$|$ 3–5 92.54
    $|t_{\text{end-search}}$(Growth period) $-$ $t_{\text{end-tweet}}$(Birth period)$|$ 6–8 74.63

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