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Unsupervised KeyPhrase Extraction Based on Multi-granular Semantics Feature Fusion

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Rough Sets (IJCRS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14481))

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Abstract

In Unsupervised Keyphrase Extraction (UKE) tasks, candidate phrases are ranked based on their similarity to the document embedding. However, This method assumes that every document focuses on only one topic. As a result, it can be difficult to distinguish the significance of potential keyphrases among different topics. Hence, it is necessary to discover a method for acquiring diversified topic information to obtain accurate key phrases. In this paper, we propose a new unsupervised key phrase extraction method (MSFFUKE) that utilizes multi-granularity semantic feature fusion. We first cluster phrases into different clusters through granulation, calculate the semantic similarity between phrases and each cluster, and take the mean to obtain the semantic features of topic granularity. Then, we obtain semantic features of phrase granularity based on the degree centrality of candidate phrases in the graph structure. Finally, we integrate semantic features of different granularity to sort candidate phrases. Three public benchmarks (Inspec, DUC 2001, SemEval 2010) are used to evaluate our model and compared it to the most advanced models currently available. The results demonstrate that our model performs better than most models and can generalize well when processing input documents from various domains and of different lengths. Another ablation study indicates that both topic granularity semantic features and phrase granularity semantic features are crucial for unsupervised keyphrase extraction tasks.

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Acknowledgements

This work was supported by the Major Program of the National Natural Science Foundation of China (Grant No.61876001, 61876157), the National Social Science Foundation of China (GrantNo.18ZDA032), the Natural Science Foundation for the Higher Education Institutions of Anhui Province of China (KJ2021A0039).

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Beijing, 230601, Anhui, People’s Republic of China

    Jie Chen, Hainan Hu, Shu Zhao & Yanping Zhang

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui, People’s Republic of China

    Jie Chen, Hainan Hu, Shu Zhao & Yanping Zhang

  3. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Beijing, 230601, Anhui, People’s Republic of China

    Jie Chen, Hainan Hu, Shu Zhao & Yanping Zhang

Authors
  1. Jie Chen
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  2. Hainan Hu
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  3. Shu Zhao
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  4. Yanping Zhang
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Corresponding author

Correspondence to Shu Zhao .

Editor information

Editors and Affiliations

  1. IRCCS Istituto Ortopedico Galeazzi, Milano, Italy

    Andrea Campagner

  2. Ghent University, Ghent, Belgium

    Oliver Urs Lenz

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Shuyin Xia

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. AGH University of Science and Technology, Kraków, Poland

    Jarosław Wąs

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

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Chen, J., Hu, H., Zhao, S., Zhang, Y. (2023). Unsupervised KeyPhrase Extraction Based on Multi-granular Semantics Feature Fusion. In: Campagner, A., Urs Lenz, O., Xia, S., Ślęzak, D., Wąs, J., Yao, J. (eds) Rough Sets. IJCRS 2023. Lecture Notes in Computer Science(), vol 14481. Springer, Cham. https://doi.org/10.1007/978-3-031-50959-9_21

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  • DOI: https://doi.org/10.1007/978-3-031-50959-9_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50958-2

  • Online ISBN: 978-3-031-50959-9

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