Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization

Sanjeev Kumar Karn, Ning Liu, Hinrich Schuetze, Oladimeji Farri


Abstract
The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist’s reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report. These tasks include acquisition of salient content from the report and generation of a concise, easily consumable IMPRESSIONS section. Prior research on radiology report summarization has focused on single-step end-to-end models – which subsume the task of salient content acquisition. To fully explore the cascade structure and explainability of radiology report summarization, we introduce two innovations. First, we design a two-step approach: extractive summarization followed by abstractive summarization. Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. Experiments on English radiology reports from two clinical sites show our novel approach leads to a more precise summary compared to single-step and to two-step-with-single-extractive-process baselines with an overall improvement in F1 score of 3-4%.
Anthology ID:
2022.acl-long.109
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:
1542–1553
Language:
URL:
https://aclanthology.org/2022.acl-long.109
DOI:
10.18653/v1/2022.acl-long.109
Bibkey:
Cite (ACL):
Sanjeev Kumar Karn, Ning Liu, Hinrich Schuetze, and Oladimeji Farri. 2022. Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1542–1553, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization (Karn et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.109.pdf