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International Conference on Web Information Systems Engineering

WISE 2017: Web Information Systems Engineering – WISE 2017 pp 135–149Cite as

Extractive Summarization via Overlap-Based Optimized Picking

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10569))

Abstract

Optimization-based methods regard summarization as a combinatorial optimization problem and formulate it as weighted linear combination of criteria metrics. However due to inconsistent criteria metrics, it is hard to set proper weights. Subjectivity problem also arises since most of them summarize original texts. In this paper, we propose overlap based greedy picking (OGP) algorithm for citation-based extractive summarization. In the algorithm, overlap is defined as a sentence containing several topics. Since including overlaps into summaires indirectly impacts on salience, summary size and content redundancy, OGP effectively avoids the problem of inconsistent metric while dynamically involving criteria into optimization. Despite of greedy method, OGP proves above \((1-1/e)\) of optimal solution. Since citation context is composed of objective evaluations, OGP also solves subjectivity problem. Our experiment results show that OGP outperforms other baseline methods. And various criteria proves effectively involved under the control of single parameter \(\beta \).

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Authors and Affiliations

  1. Beijing Institute of Technology, Beijing, China

    Gaokun Dai & Zhendong Niu

Authors
  1. Gaokun Dai
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  2. Zhendong Niu
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Corresponding author

Correspondence to Gaokun Dai .

Editor information

Editors and Affiliations

  1. University of Sydney, Darlington, NSW, Australia

    Athman Bouguettaya

  2. Zhejiang University, Hangzhou, China

    Yunjun Gao

  3. Institute of Computing for Physics and Technology, Protvino, Russia

    Andrey Klimenko

  4. Nanyang Technological University, Singapore, Singapore

    Lu Chen

  5. King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Xiangliang Zhang

  6. Institute of Computing for Physics and Technology, Protvino, Russia

    Fedor Dzerzhinskiy

  7. Shanghai Jiao Tong University, Minhang Qu, China

    Weijia Jia

  8. Institute of Computing for Physics and Technology, Protvino, Russia

    Stanislav V. Klimenko

  9. City University of Hong Kong, Kowloon, Hong Kong

    Qing Li

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Dai, G., Niu, Z. (2017). Extractive Summarization via Overlap-Based Optimized Picking. In: Bouguettaya, A., et al. Web Information Systems Engineering – WISE 2017. WISE 2017. Lecture Notes in Computer Science(), vol 10569. Springer, Cham. https://doi.org/10.1007/978-3-319-68783-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-68783-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68782-7

  • Online ISBN: 978-3-319-68783-4

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