Personalized Time-Aware Tweets Summarization

Example entity linking for tweets, to support tweets summarization

To appear as full paper at SIGIR 2013.

In this paper we focus on selecting meaningful tweets given a user’s interests. Specifically, we consider the task of time-aware tweets summarization, based on a user’s history and collaborative social influences from “social circles.” We propose a time-aware user behavior model, the Tweet Propagation Model (TPM), in which we infer dynamic probabilistic distributions over interests and topics. We then explicitly consider novelty, coverage, and diversity to arrive at an iterative optimization algorithm for selecting tweets. Experimental results validate the effectiveness of our personalized time-aware tweets summarization method based on TPM.

I’ll post a pre-print of the paper asap.

  • [PDF] Z. Ren, S. Liang, E. Meij, and M. de Rijke, “Personalized time-aware tweets summarization,” in Proceedings of the 36th international acm sigir conference on research and development in information retrieval, 2013, pp. 513-522.
    [Bibtex]
    @inproceedings{SIGIR:2013:Ren,
    Author = {Ren, Zhaochun and Liang, Shangsong and Meij, Edgar and de Rijke, Maarten},
    Booktitle = {Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    Date-Added = {2014-05-16 06:24:55 +0000},
    Date-Modified = {2014-05-16 06:25:35 +0000},
    Pages = {513--522},
    Series = {SIGIR '13},
    Title = {Personalized Time-aware Tweets Summarization},
    Year = {2013},
    Bdsk-Url-1 = {http://doi.acm.org/10.1145/2484028.2484052},
    Bdsk-Url-2 = {http://dx.doi.org/10.1145/2484028.2484052}}

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