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The Pareto Principle Is Everywhere: Finding Informative Sentences for Opinion Summarization Through Leader Detection

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Recommendation and Search in Social Networks

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Most previous works on opinion summarization focus on summarizing sentiment polarity distribution toward different aspects of an entity (e.g., battery life and screen of a mobile phone). However, users’ demand may be more beyond this kind of opinion summarization. Besides such coarse-grained summarization on aspects, one may prefer to read detailed but concise text of the opinion data for more information. In this paper, we propose a new framework for opinion summarization. Our goal is to assist users to get helpful opinion suggestions from reviews by only reading a short summary with a few informative sentences, where the quality of summary is evaluated in terms of both aspect coverage and viewpoints preservation. More specifically, we formulate the informative sentence selection problem in opinion summarization as a community leader detection problem, where a community consists of a cluster of sentences toward the same aspect of an entity and leaders can be considered as the most informative sentences of the corresponding aspect. We develop two effective algorithms to identify communities and leaders. Reviews of six products from Amazon.com are used to verify the effectiveness of our method for opinion summarization.

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Notes

  1. 1.

    http://www.tripadvisor.com/.

  2. 2.

    Note that \(|\mathcal {N}_{k}(s)|\) can be larger than \(k\) since there could be the event of ties (i.e., a set of neighbors have the same similarity to \(s\)).

  3. 3.

    Available at http://sites.google.com/site/linhongi2r/data-and-code.

  4. 4.

    A longer summary is more likely to provide better information but is less concise.

  5. 5.

    ROUGE-N is a popular toolkit which measures the quality of a summary by comparing it to other reference summaries using \(n\)-gram co-occurrence.

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Acknowledgments

This work is partially supported by DARPA under grant Number W911NF-12-1-0034.

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Authors and Affiliations

  1. Information Sciences Institute, Los Angeles, USA

    Linhong Zhu

  2. Institute for Infocomm Research, Singapore, Singapore

    Sheng Gao, Sinno Jialin Pan & Haizhou Li

  3. University of Southern California, Los Angeles, USA

    Dingxiong Deng & Cyrus Shahabi

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  1. Linhong Zhu
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  2. Sheng Gao
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  3. Sinno Jialin Pan
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  4. Haizhou Li
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  5. Dingxiong Deng
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  6. Cyrus Shahabi
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Corresponding author

Correspondence to Linhong Zhu .

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Editors and Affiliations

  1. Department of Computer Engineering, Bilkent University, Ankara, Turkey

    Özgür Ulusoy

  2. Statistics and Computer Information Systems, Baruch College, CUNY, New York, New York, USA

    Abdullah Uz Tansel

  3. Department of Computer Engineering, Bilkent University, Ankara, Turkey

    Erol Arkun

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Zhu, L., Gao, S., Pan, S.J., Li, H., Deng, D., Shahabi, C. (2015). The Pareto Principle Is Everywhere: Finding Informative Sentences for Opinion Summarization Through Leader Detection. In: Ulusoy, Ö., Tansel, A., Arkun, E. (eds) Recommendation and Search in Social Networks. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-14379-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-14379-8_9

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  • Online ISBN: 978-3-319-14379-8

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