Text embeddings are typically evaluated on a limited set of tasks, which are constrained by
language, domain, and task diversity. To address these limitations and provide a more …

This article provides a systematic up-to-date survey of automatic summarization techniques,
datasets, models, and evaluation methods in the legal domain. Through specific source …

We present some initial results of a large-scale Italian project called PRODIGIT which aims
to support tax judges and lawyers through digital technology, focusing on AI. We have …

We propose and evaluate an automated pipeline for discovering significant topics from legal
decision texts by passing features synthesized with topic models through penalized …

Legal documents are often long and unstructured, making them challenging and time-
consuming to apprehend. An automatic system that can identify relevant entities and labels …

Legal document summarization aims to provide a clear understanding of the main points
and arguments in a legal document, contributing to the efficiency of the judicial system. In …

Legal knowledge involves multidimensional heterogeneous knowledge such as legal
provisions, judicial interpretations, judicial cases, and defenses, which requires extremely …

The ever-increasing volume of electronic legal documents calls for effective, language-
specific summarization and headline generation techniques to make legal content more …

The rapid growth in the volume and complexity of legal documents has created an urgent
need for automated tools to help legal professionals efficiently extract and summarize critical …

This challenge consists of three classification tasks, in the context of argument mining in the
legal domain. The tasks are based on a dataset of 225 Italian decisions on Value Added …