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WS-Rank: Bringing Sentences into Graph for Keyword Extraction

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9932))

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Abstract

Graph-based method is one of the most efficient unsupervised ways to extract keyword from a single web text. However, rarely did the previous graph-based methods consider the sentence importance. In this paper, we propose a graph-based keyword extractor WS-Rank which brings sentences into graph where sentences are distinctively treated according to their importance. The candidate keywords are extracted through the voting mechanism between words and sentences. To evaluate the experiment, we compare our method with TextRank, a graph-based method which uses the logic distribution relationship only between words. Experiment on 13702 web texts carried out shows that WS-Rank achieves more ideal results with an average F-score of 25.20 %.

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Notes

  1. 1.

    http://nlp.csai.tsinghua.edu.cn/~lzy/#Data.

  2. 2.

    http://ictclas.nlpir.org/.

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Authors and Affiliations

  1. Communication and Information Security Lab, Shenzhen Graduate School, Institute of Big Data Technologies, Peking University, Shenzhen, China

    Fan Yang, Yue-Sheng Zhu & Yu-Jia Ma

Authors
  1. Fan Yang
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  2. Yue-Sheng Zhu
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  3. Yu-Jia Ma
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Corresponding author

Correspondence to Yue-Sheng Zhu .

Editor information

Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

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© 2016 Springer International Publishing Switzerland

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Yang, F., Zhu, YS., Ma, YJ. (2016). WS-Rank: Bringing Sentences into Graph for Keyword Extraction. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9932. Springer, Cham. https://doi.org/10.1007/978-3-319-45817-5_49

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  • DOI: https://doi.org/10.1007/978-3-319-45817-5_49

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45816-8

  • Online ISBN: 978-3-319-45817-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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