# Legal Summarisation through LLMs: The PRODIGIT Project

arXiv: https://arxiv.org/abs/2308.04416 · HTML: https://mlsanity.com/paper/2308.04416/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 8 of 8)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Can GPT-3 Perform Statutory Reasoning?](https://mlsanity.com/paper/2302.06100/) (2023)
- [Legal Prompting: Teaching a Language Model to Think Like a Lawyer](https://mlsanity.com/paper/2212.01326/) (2022)
- [Legal Prompt Engineering for Multilingual Legal Judgement Prediction](https://mlsanity.com/paper/2212.02199/) (2022)
- [Law Informs Code: A Legal Informatics Approach to Aligning Artificial Intelligence with Humans](https://mlsanity.com/paper/2209.13020/) (2022)
- [IT5: Text-to-text Pretraining for Italian Language Understanding and Generation](https://mlsanity.com/paper/2203.03759/) (2022)
- [Large Language Models as Fiduciaries: A Case Study Toward Robustly Communicating With Artificial Intelligence Through Legal Standards](https://mlsanity.com/paper/2301.10095/) (2023)

## Built on by (most cited first, 10 of 10)

- [Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges](https://mlsanity.com/paper/2410.21306/) (2024)
- [Legal Minds, Algorithmic Decisions: How LLMs Apply Constitutional Principles in Complex Scenarios](https://mlsanity.com/paper/2407.19760/) (2024)
- [When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance](https://mlsanity.com/paper/2507.07748/) (2025)
- [Human Centered AI for Indian Legal Text Analytics](https://mlsanity.com/paper/2403.10944/) (2024)
- [Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval](https://mlsanity.com/paper/2410.12154/) (2024)
- [Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study](https://mlsanity.com/paper/2412.06272/) (2024)
- [Legal NLP Meets MiCAR: Advancing the Analysis of Crypto White Papers](https://mlsanity.com/paper/2310.10333/) (2023)
- [From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control](https://mlsanity.com/paper/2607.03325/) (2026)
- [SinBrief: A Hybrid Framework for Abstractive Text Summarisation of Sinhala Legal Documents](https://mlsanity.com/paper/2609.32397/) (2026)
- [Which LLM to pick? Online Active Model Selection for Large Language Models](https://mlsanity.com/paper/2610.01592/) (2026)

## Abstract (arXiv)

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 focused on generation of summaries of judicial decisions and on the extraction of related information, such as the identification of legal issues and decision-making criteria, and the specification of keywords. To this end, we have deployed and evaluated different tools and approaches to extractive and abstractive summarisation. We have applied LLMs, and particularly on GPT4, which has enabled us to obtain results that proved satisfactory, according to an evaluation by expert tax judges and lawyers. On this basis, a prototype application is being built which will be made publicly available.