Tasks performed in the legal domain through Deep Learning: A bibliometric review (1987–2020)

Abstract

Deep Learning (DL) has become the state-of-the-art method for Natural Language Processing (NLP). During the last 5 years DL became the primary Artificial Intelligence (AI) method in the legal domain. In this work we provide a systematic bibliometric review of the publications that have utilized DL as the primary methodology. In particular we analyzed the performed objectives (performed tasks), the corpus utilized to train the models and promising areas of research. The sample includes a total of 137 works published between 1987 and 2020. This analysis starts with the first DL models (formerly Neural Networks) in the legal domain until the latest articles in the ongoing year. Our results show an increment of 300% on the total number of publications during the last 5 years, mainly on information extraction and classification tasks. Moreover, classification is the category with most publications with 39% of the total sample. Finally, we have identified that summarization and text generation as promising areas of research. These findings show that DL in the legal domain is currently in a growing stage, and hence it will be a promising topic of research in the coming years.