Legal Summarization: to each Court its own model
Authors: Flavia Achena, David Preti, Davide Venditti, Leonardo Ranaldi, Cristina Giannone, Fabio Massimo Zanzotto, Andrea Favalli, Raniero Romagnoli (Almawave, Università di Roma Tor Vergata, 2023)
Topic: In-domain IR and IE

Abstract
In the Italian Civil Law System, easily accessing legal judgments through massime is crucial. In this work, we compare extractive summarization models to produce massime in two Italian courts: the Constitutional Court and the Supreme Court. The aim of our study is to assess the effectiveness and efficiency of these models in summarizing the decisions of the two courts. Through a comprehensive analysis of two large datasets, we evaluate the quality of the summaries generated by each model and their ability to capture the key legal principles and linguistic features present in the courts’ decisions.

Introduction
In civil and common law systems, accessing legal judgments to retrieve legal decisions is crucial when lawyers have to defend clients, prosecutors have to build cases, and judges have to draw decisions. To ensure widespread information on the decisions of the courts, in Italy, for this purpose, a specific body draws up massime. These massime present, in a short but detailed way, a legal principle present in judgments. Hence, justice professionals can read these massime instead of the complete legal decisions. The process of analyzing judgments and extracting relevant sentences can be significantly simplified through the use of pre-trained models, which serve as versatile universal sentence/text encoders, capable of addressing various downstream tasks, including summarization. These models consistently outperform other approaches, especially after fine-tuning or domain-adaptation. However, despite the success of pre-trained transformers in other summarization tasks, the task of producing massime is challenging for current extractive and abstractive summarization systems. Unlike standard summaries, massime must follow rigorous specifications in some courts. Extractive and abstractive summarization datasets and related systems, in contrast, aim to reduce the size of a text while preserving its overall meaning. However, this approach differs from the specific requirements of creating massime. Additionally, legal texts are often extensive, further increasing the summarization task’s complexity. Identifying the portions of the text that contain the relevant information to be reported in the massime becomes challenging due to their length. Legal document summarization has seen rapid progress in recent years, and several approaches have been proposed to manage this kind of data, ranging from fine-tuned Transformer models on the legal domain, Reinforcement Learning to Generative Models. In this paper, we compare extractive summarization models to produce massime in two different contexts: the Constitutional Court (Corte Costituzionale) and the Supreme Court (Corte di Cassazione). We discuss similarities and differences regarding massime and how the type of court impacts the data in terms of their availability and privacy management. Then, we propose two models tailored to the specific type of courts, discussing the approaches we implemented to circumvent the issues related to lengthy documents. Results of the experiments confirm that producing massime is a real challenge even for dedicated systems. Hence, these systems should be designed as facilitators in a human-in-the-loop environment.

Conclusion
In conclusion, while we did not achieve outstanding results, our efforts shed light on the intricacies of this challenging problem. During our analysis, we also noticed notable differences between the two courts, which further emphasizes the complexity of generating accurate massime. We find it particularly intriguing to explore the factors contributing to these variations and understand how they impact the summarization process. Despite the challenges, we remain committed to refining our approach and exploring innovative techniques. Recent advances in the field further motivate us to seek a proper solution that addresses data privacy concerns and significantly improves the task of summarization in the legal field for the Italian language.