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Twitter summarization with social-temporal context

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Abstract

Twitter is one of the most popular social media platforms for online users to create and share information. Tweets are short, informal, and large-scale, which makes it difficult for online users to find reliable and useful information, arising the problem of Twitter summarization. On the one hand, tweets are short and highly unstructured, which makes traditional document summarization methods difficult to handle Twitter data. On the other hand, Twitter provides rich social-temporal context beyond texts, bringing about new opportunities. In this paper, we investigate how to exploit social-temporal context for Twitter summarization. In particular, we provide a methodology to model temporal context globally and locally, and propose a novel unsupervised summarization framework with social-temporal context for Twitter data. To assess the proposed framework, we manually label a real-world Twitter dataset. Experimental results from the dataset demonstrate the importance of social-temporal context in Twitter summarization.

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Notes

  1. https://wiki.engr.illinois.edu/display/forward/Dataset-UDI-TwitterCrawl-Aug2012

  2. http://duc.nist.gov/

  3. http://www.nist.gov/tac

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Acknowledgments

This work was supported in part by National Key Basic Research and Development Program of China (973 Program) under Grant 2013CB329304,2013CB329301, National Natural Science Foundation of China (Grant No:61100123,61472277), Ministry of Education Fund of China for the Doctoral (Grant No:20110032120040) and Tianjin Younger Natural Science Foundation (Grant No:14JCQNJC00400).

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Authors and Affiliations

  1. Tianjin Key Laboratory of Cognitive Computing and Application, School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Ruifang He, Guangchuan Yu, Qinghua Hu & Jianwu Dang

  2. School of Computer Science and Technology, Peking University, Peking, 100871, China

    Yang Liu

  3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 85281, Tempe, AZ, USA

    Jiliang Tang

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  1. Ruifang He
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  2. Yang Liu
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  3. Guangchuan Yu
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  4. Jiliang Tang
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  5. Qinghua Hu
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  6. Jianwu Dang
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Correspondence to Ruifang He.

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He, R., Liu, Y., Yu, G. et al. Twitter summarization with social-temporal context. World Wide Web 20, 267–290 (2017). https://doi.org/10.1007/s11280-016-0386-0

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