Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
Sanjeev Kumar Karn, Ning Liu, Hinrich Schuetze, Oladimeji Farri · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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-stepwith-single-extractive-process baselines with an overall improvement in F1 score of 3-4%.