@incollection{Zhang2022a,
 title = {A {{Decade}} of {{Legal Argumentation Mining}}: {{Datasets}} and {{Approaches}}},
 author = {Zhang, Gechuan and Nulty, Paul and Lillis, David},
 translator = {Rosso, Paolo and Basile, Valerio and Mart{\'i}nez, Raquel and M{\'e}tais, Elisabeth and Meziane, Farid},
 year = {2022},
 series = {Natural {{Language Processing}} and {{Information Systems}}},
 pages = {240--252},
 publisher = {{Springer International Publishing}},
 address = {{Cham}},
 doi = {10.1007/978-3-031-08473-7_22},
 abstract = {The growing research field of argumentation mining (AM) in the past ten years has made it a popular topic in Natural Language Processing. However, there are still limited studies focusing on AM in the context of legal text (Legal AM), despite the fact that legal text analysis more generally has received much attention as an interdisciplinary field of traditional humanities and data science. The goal of this work is to provide a critical data-driven analysis of the current situation in Legal AM. After outlining the background of this topic, we explore the availability of annotated datasets and the mechanisms by which these are created. This includes a discussion of how arguments and their relationships can be modelled, as well as a number of different approaches to divide the overall Legal AM task into constituent sub-tasks. Finally we review the dominant approaches that have been applied to this task in the past decade, and outline some future directions for Legal AM research.},
 isbn = {978-3-031-08473-7},
}