Bulletin of Electrical Engineering and Informatics

Received May 24, 2022 Revised Aug 6, 2022 Accepted Sep 27, 2022 Automatic keyphrases extraction (AKE) is a principal task in natural language processing (NLP). Several techniques have been exploited to improve the process of extracting keyphrases from documents. Deep learning (DL) algorithms are the latest techniques used in prediction and extraction of keyphrases. DL is one of the most complex types of machine learning, relying on the use of artificial neural networks to make the machine follow the same decision-making path as the human brain. In this paper, we present a review of deep learning-based methods for AKE from documents, to highlight their contribution to improving keyphrase extraction performance. This review will also provide researchers with a collection of data and information on the mechanisms of deep learning algorithms in the AKE domain. This will allow them to solve problems encountered by AKE approaches and propose new methods for improving key-extraction performance.

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