# Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation

arXiv: https://arxiv.org/abs/2604.19331 · HTML: https://mlsanity.com/paper/2604.19331/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [Argumentative Large Language Models for Explainable and Contestable Claim Verification](https://mlsanity.com/paper/2405.02079/) (2024)
- [Interactive Explanations by Conflict Resolution via Argumentative Exchanges](https://mlsanity.com/paper/2303.15022/) (2023)
- [Can Large Language Models perform Relation-based Argument Mining?](https://mlsanity.com/paper/2402.11243/) (2024)
- [Which Side Are You On? A Multi-task Dataset for End-to-End Argument Summarisation and Evaluation](https://mlsanity.com/paper/2406.03151/) (2024)
- [OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset](https://mlsanity.com/paper/2406.14657/) (2024)
- [Limited Generalizability in Argument Mining: State-Of-The-Art Models Learn Datasets, Not Arguments](https://mlsanity.com/paper/2505.22137/) (2025)
- [AMELIA: A Family of Multi-task End-to-end Language Models for Argumentation](https://mlsanity.com/paper/2508.17926/) (2025)

## Built on by (most cited first, 1 of 1)

- [Do LLM Debates Repeat Arguments Differently Across Languages?](https://mlsanity.com/paper/2607.23442/) (2026)

## Abstract (arXiv)

Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of such debates mean that outside audiences struggle to engage. Meanwhile, Large Language Models (LLMs) have been shown to enable automated summarisation at scale. While summaries of debates can make parliamentary procedures more accessible, evaluating whether these summaries faithfully communicate argumentative content remains challenging. Existing automated summarisation metrics have been shown to correlate poorly with human judgements of consistency (i.e., faithfulness or alignment between summary and source). In this work, we propose a formal framework for evaluating parliamentary debate summaries that grounds argument structures in the contested proposals up for debate. Our novel approach, driven by computational argumentation, focuses the evaluation on formal properties concerning the faithful preservation of the reasoning presented to justify or oppose policy outcomes. We demonstrate our methods using a case-study of debates from the European Parliament and associated LLM-driven summaries.