Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and Applications

Abstract

The surge in legal text production has amplified the workload for legal professionals, making many tasks repetitive and time-consuming. Furthermore, the complexity and specialized language of legal documents pose challenges not just for those in the legal domain but also for the general public. This emphasizes the potential role and impact of Legal Natural Language Processing (Legal NLP). Although advancements have been made in this domain, particularly after 2015 with the advent of Deep Learning and Large Language Models (LLMs), a systematic exploration of this progress until 2022 is nonexistent. In this research, we perform a Systematic Mapping Study (SMS) to bridge this gap. We aim to provide a descriptive statistical analysis of the Legal NLP research between 2015 and 2022. Categorize and sub-categorize primary publications based on their research problems. Identify limitations and areas of improvement in current research. Using a robust search methodology across four reputable indexers, we filtered 536 papers down to 75 pivotal articles. Our findings reveal the diverse methods employed for tasks such as Multiclass Classification, Summarization, and Question Answering in the Legal NLP field. We also highlight resources, challenges, and gaps in current methodologies and emphasize the need for curated datasets, ontologies, and a focus on inherent difficulties like data accessibility. As the legal sector gradually embraces Natural Language Processing (NLP), understanding the capabilities and limitations of Legal NLP becomes vital for ensuring efficient and ethical application. The research offers insights for both Legal NLP researchers and the broader legal community, advocating for continued advancements in automation while also addressing ethical concerns.

Keywords

• Systematic-mapping-study
• legal-NLP
• deep learning