Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations
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Abstract (arXiv)
Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are used as summaries presenting all the steps of a judicial case. Information concerning parties' identity, jurisdiction in charge of administering the case, and some information relating to the nature and the course of the preceding are available through plumitifs. They are publicly accessible but barely understandable; they are written using abbreviations and referring to provisions from the Criminal Code of Canada, which makes them hard to reason about. In this paper, we propose a simple yet efficient multi-source language generation architecture that leverages both the plumitif and the Criminal Code's content to generate intelligible plumitifs descriptions. It goes without saying that ethical considerations rise with these sensitive documents made readable and available at scale, legitimate concerns that we address in this paper.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Nov 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 2.
5 papers.
- 2021Underreporting of errors in NLG output, and what to do about itcited by 19
- 2024Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generationcited by 2
- 2023RISC: Generating Realistic Synthetic Bilingual Insurance Contractcited by 1
- 2026Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generationcited by 0
- 2026A Human-Centric Evaluation of a Retrieval-Augmented Generation System for Explaining Quebec Insurance Contractscited by 0
- 2026A Human-Centric Evaluation of a Retrieval-Augmented Generation System for Explaining Quebec Insurance Contracts17 Jul 2026
- 2026Benchmarking Large Language Models for Quebec Insurance: From Closed-Book to Retrieval-Augmented Generation8 Mar 2026
- 2024Quebec Automobile Insurance Question-Answering With Retrieval-Augmented Generation12 Oct 2024
- 2023RISC: Generating Realistic Synthetic Bilingual Insurance Contract9 Apr 2023
- 2021Underreporting of errors in NLG output, and what to do about it2 Aug 2021