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Legal Summarisation through LLMs: The PRODIGIT Project

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arxiv 2308.04416 v1 pith:UJPMIOS3 submitted 2023-08-04 cs.CL

classification cs.CL
keywords judgeslawyerslegalllmsprodigitprojectresultssummarisation
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A cost-efficient DeepSeek-V3 pipeline extracts IRAC-grounded issue-level XML from ~330k Italian tax judgments and cuts citation hallucinations from 11.7% to 0.9% via Linkoln matching, validated by two tax-law PhDs on ...

  2. When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance

    cs.CL 2025-07 conditional novelty 4.0 of 10

    A literature review that classifies LLM-for-law research using a dual-lens taxonomy of Toulmin argumentation components and legal practitioner roles.

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