# Question-Answering Approach to Evaluating Legal Summaries

arXiv: https://arxiv.org/abs/2309.15016 · HTML: https://mlsanity.com/paper/2309.15016/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 14 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [Is ChatGPT a Good NLG Evaluator? A Preliminary Study](https://mlsanity.com/paper/2303.04048/) (2023)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://mlsanity.com/paper/2005.03754/) (2020)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [TopiOCQA: Open-domain Conversational Question Answering with Topic Switching](https://mlsanity.com/paper/2110.00768/) (2021)
- [Answers Unite! Unsupervised Metrics for Reinforced Summarization Models](https://mlsanity.com/paper/1909.01610/) (2019)
- [Question Answering as an Automatic Evaluation Metric for News Article Summarization](https://mlsanity.com/paper/1906.00318/) (2019)
- [Multi-granularity Argument Mining in Legal Texts](https://mlsanity.com/paper/2210.09472/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Comprehensive Survey on Legal Summarization: Challenges and Future Directions](https://mlsanity.com/paper/2501.17830/) (2025)
- [Summarisation of German Judgments in conjunction with a Class-based Evaluation](https://mlsanity.com/paper/2505.05947/) (2025)
- [Na Pr'atica, qual IA Entende o Direito? Um Estudo Experimental com IAs Generalistas e uma IA Jur'idica](https://mlsanity.com/paper/2510.18108/) (2025)
- [Supporting Humans in Evaluating AI Summaries of Legal Depositions](https://mlsanity.com/paper/2601.15182/) (2026)
- [From Judgments to Issues: Structured Extraction of Legal Reasoning with Citation-Hallucination Control](https://mlsanity.com/paper/2607.03325/) (2026)

## Abstract (arXiv)

Traditional evaluation metrics like ROUGE compare lexical overlap between the reference and generated summaries without taking argumentative structure into account, which is important for legal summaries. In this paper, we propose a novel legal summarization evaluation framework that utilizes GPT-4 to generate a set of question-answer pairs that cover main points and information in the reference summary. GPT-4 is then used to generate answers based on the generated summary for the questions from the reference summary. Finally, GPT-4 grades the answers from the reference summary and the generated summary. We examined the correlation between GPT-4 grading with human grading. The results suggest that this question-answering approach with GPT-4 can be a useful tool for gauging the quality of the summary.