Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation

Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Damir Juric, Jack Flann, Ehud Reiter, Anya Belz, Aleksandar Savkov


Abstract
In recent years, machine learning models have rapidly become better at generating clinical consultation notes; yet, there is little work on how to properly evaluate the generated consultation notes to understand the impact they may have on both the clinician using them and the patient’s clinical safety.To address this we present an extensive human evaluation study of consultation notes where 5 clinicians (i) listen to 57 mock consultations, (ii) write their own notes, (iii) post-edit a number of automatically generated notes, and (iv) extract all the errors, both quantitative and qualitative. We then carry out a correlation study with 18 automatic quality metrics and the human judgements. We find that a simple, character-based Levenshtein distance metric performs on par if not better than common model-based metrics like BertScore. All our findings and annotations are open-sourced.
Anthology ID:
2022.acl-long.394
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5739–5754
Language:
URL:
https://aclanthology.org/2022.acl-long.394
DOI:
10.18653/v1/2022.acl-long.394
Bibkey:
Cite (ACL):
Francesco Moramarco, Alex Papadopoulos Korfiatis, Mark Perera, Damir Juric, Jack Flann, Ehud Reiter, Anya Belz, and Aleksandar Savkov. 2022. Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5739–5754, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation (Moramarco et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.394.pdf
Video:
 https://aclanthology.org/2022.acl-long.394.mp4
Data
CNN/Daily Mail