# A preliminary study on evaluating Consultation Notes with Post-Editing

arXiv: https://arxiv.org/abs/2104.04402 · HTML: https://mlsanity.com/paper/2104.04402/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 1 of 1)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)

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

- [MeetingBank: A Benchmark Dataset for Meeting Summarization](https://mlsanity.com/paper/2305.17529/) (2023)
- [Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation](https://mlsanity.com/paper/2204.00447/) (2022)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [Hallucination Detection in Large Language Models with Metamorphic Relations](https://mlsanity.com/paper/2502.15844/) (2025)
- [User-Driven Research of Medical Note Generation Software](https://mlsanity.com/paper/2205.02549/) (2022)
- [Extrinsically-Focused Evaluation of Omissions in Medical Summarization](https://mlsanity.com/paper/2311.08303/) (2023)
- [Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data](https://mlsanity.com/paper/2410.12845/) (2024)
- [An Exploration of Post-Editing Effectiveness in Text Summarization](https://mlsanity.com/paper/2206.06383/) (2022)
- [CLARITY: Clinical Assistant for Routing, Inference, and Triage](https://mlsanity.com/paper/2510.02463/) (2025)
- [How to Evaluate Medical AI](https://mlsanity.com/paper/2509.11941/) (2025)

## Abstract (arXiv)

Automatic summarisation has the potential to aid physicians in streamlining clerical tasks such as note taking. But it is notoriously difficult to evaluate these systems and demonstrate that they are safe to be used in a clinical setting. To circumvent this issue, we propose a semi-automatic approach whereby physicians post-edit generated notes before submitting them. We conduct a preliminary study on the time saving of automatically generated consultation notes with post-editing. Our evaluators are asked to listen to mock consultations and to post-edit three generated notes. We time this and find that it is faster than writing the note from scratch. We present insights and lessons learnt from this experiment.