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Improving Keyphrase Extraction Using LL-Ranking

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Advances in Intelligent Systems and Computing IV (CSIT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1080))

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Abstract

Keyphrases provide a concise representation of the main content of a document and can be effectively used within information retrieval systems. In the paper, we deal with the keyphrase extraction problem when a given number of keyphrases for a text should be extracted. The research is focused on the keyphrase candidates ranking stage. In the domain, the question remains open of whether the keyphrase extraction quality can be improved by putting limits on the number of phrases of different lengths extracted during candidate ranking. We assume that the quality of resulting keyphrases can be enhanced if we introduce \(\underline{L}\)imitations on the number of phrases of specific \(\underline{L}\)engths in the resulting set (LL-ranking strategy). The experiments are performed on the well-known INSPEC dataset of scientific abstracts. The obtained results show that the proposed limitations help to significantly increase the quality of extracted keyphrases in terms of Precision and F1.

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Acknowledgments

The reported study was partially funded by RFBR (Russian Fund of Basic Research) according to the research projects No. 16-37-00430 mol_a (for Svetlana Popova) and No. 18-07-01441 a (for Mikhail Alexandrov).

Author information

Authors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Svetlana Popova & John Cardiff

  2. St. Petersburg State University, St. Petersburg, Russia

    Svetlana Popova

  3. RANEPA, Moscow, Russia

    Vera Danilova & Mikhail Alexandrov

  4. Autonomous University of Barcelona, Barcelona, Spain

    Mikhail Alexandrov

Authors
  1. Svetlana Popova
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  2. Vera Danilova
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  3. Mikhail Alexandrov
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  4. John Cardiff
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Corresponding author

Correspondence to Svetlana Popova .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

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Popova, S., Danilova, V., Alexandrov, M., Cardiff, J. (2020). Improving Keyphrase Extraction Using LL-Ranking. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing IV. CSIT 2019. Advances in Intelligent Systems and Computing, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-33695-0_38

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