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Summarizing Weibo with Topics Compression

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Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10762))

Abstract

Extractive text summarization aims at selecting a small subset of sentences so that the contents and meaning of the original document are best preserved. In this paper we describe an unsupervised approach to extractive summarization. It combines hierarchical topic modeling (TM) with the Minimal Description Length (MDL) principle and applies them to Chinese language. Our summarizer strives to extract information that provides the best description of text topics in terms of MDL. This model is applied to the NLPCC 2015 Shared Task of Weibo-Oriented Chinese News Summarization [1], where Chinese texts from news articles were summarized with the goal of creating short meaningful messages for Weibo (Sina Weibo is a Chinese microblogging website, one of the most popular sites in China.) [2]. The experimental results disclose superiority of our approach over other summarizers from the NLPCC 2015 competition.

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Notes

  1. 1.

    http://ictclas.nlpir.org/.

  2. 2.

    Because our system did not participate in the NLPCC competition, all experiments were re-run by ourselves.

  3. 3.

    ranked first by Rouge, F-measure.

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Author information

Authors and Affiliations

  1. Department of Software Engineering, Shamoon Engineering College, Beer Sheva, Israel

    Marina Litvak & Natalia Vanetik

  2. Department of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China

    Lei Li

Authors
  1. Marina Litvak
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  2. Natalia Vanetik
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  3. Lei Li
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Corresponding author

Correspondence to Natalia Vanetik .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Litvak, M., Vanetik, N., Li, L. (2018). Summarizing Weibo with Topics Compression. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_39

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  • DOI: https://doi.org/10.1007/978-3-319-77116-8_39

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  • Online ISBN: 978-3-319-77116-8

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