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One-to-many comparative summarization for patents

Author

Listed:
  • Zheng Liu

    (Nanjing University of Posts and Telecommunications)

  • Jialing Zhang

    (Nanjing University of Posts and Telecommunications)

  • Tingting Qin

    (Nanjing University of Posts and Telecommunications)

  • Yanwen Qu

    (Jiangxi Normal University)

  • Yun Li

    (Nanjing University of Posts and Telecommunications)

Abstract

Patents bring technology companies commercial values in modern business operations. However, companies have to bear the high cost of handling patent applications or infringement cases. A common yet expensive task among these jobs is to analyze relevant patent literature. Lengthy and technically complicated patents require a large number of human efforts. This paper focuses on automatically analyzing the similar contents between a patent and its relevant literature, relevant patents specifically, to help experts review the similarities among these patents. We formulate this as a one-to-many document comparison problem by generating a comparative summary of a given patent and its relevant patents. We extract essential technical features from semantic dependency trees based on sentences in claims and construct a multi-relational graph to model the relevance between features and patents. The key to generating the comparative summary is selecting comparative essential technical features, which we formulate as an optimization problem and solve by a fast greedy algorithm. Experiments on real-world datasets and case studies demonstrate the effectiveness and efficiency of the proposed methods.

Suggested Citation

  • Zheng Liu & Jialing Zhang & Tingting Qin & Yanwen Qu & Yun Li, 2022. "One-to-many comparative summarization for patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1969-1993, April.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:4:d:10.1007_s11192-022-04307-8
    DOI: 10.1007/s11192-022-04307-8
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    References listed on IDEAS

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    1. Lea Helmers & Franziska Horn & Franziska Biegler & Tim Oppermann & Klaus-Robert Müller, 2019. "Automating the search for a patent’s prior art with a full text similarity search," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-17, March.
    2. Cinthia M. Souza & Magali R. G. Meireles & Paulo E. M. Almeida, 2021. "A comparative study of abstractive and extractive summarization techniques to label subgroups on patent dataset," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 135-156, January.
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