Abstract
Keyphrase extraction is a fundamental task in information management, which is often used as a preliminary step in various information retrieval and natural language processing tasks. The main contribution of this paper lies in providing a comparative assessment of prominent multilingual unsupervised keyphrase extraction methods that build on statistical (RAKE, YAKE), graph-based (TextRank, SingleRank) and deep learning (KeyBERT) methods. For the experimentations reported in this paper, we employ well-known datasets designed for keyphrase extraction from five different natural languages (English, French, Spanish, Portuguese and Polish). We use the F1 score and a partial match evaluation framework, aiming to investigate whether the number of terms of the documents and the language of each dataset affect the accuracy of the selected methods. Our experimental results reveal a set of insights about the suitability of the selected methods in texts of different sizes, as well as the performance of these methods in datasets of different languages.
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Acknowledgments
The work presented in this paper is supported by the inPOINT project (https://inpoint-project.eu/), which is co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (Project id: T2EDK- 04389).
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Giarelis, N., Kanakaris, N., Karacapilidis, N. (2021). A Comparative Assessment of State-Of-The-Art Methods for Multilingual Unsupervised Keyphrase Extraction. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_50
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DOI: https://doi.org/10.1007/978-3-030-79150-6_50
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