Donghee Lee, Soonchan Kwon, Beakcheol Jang, Journal of Internet Computing and Services, Vol. 24, No. 4, pp. 77-84, Aug. 2023
10.7472/jksii.2023.24.4.77, Full Text:  HTML
Keywords: Keyword Extraction, NLP, deeplearning, Biomedicine

Abstract

Given the growing volume of biomedical papers, the ability to efficiently extract keywords has become crucial for accessing and responding to important information in the literature. In this study, we conduct a comprehensive evaluation of different unsupervised learning-based models and BERT-based models for keyword extraction in the biomedical field. Our experimental findings reveal that the BioBERT model, trained on biomedical-specific data, achieves the highest performance. This study offers precise and dependable insights to guide forthcoming research in biomedical keyword extraction. By establishing a well-suited experimental framework and conducting thorough comparisons and analyses of diverse models, we have furnished essential information. Furthermore, we anticipate extending our contributions to other domains by providing comparative experiments and practical guidelines for effective keyword extraction.

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