论文标题
多步放射学报告摘要的可区分多代理参与者评论
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
论文作者
论文摘要
放射学报告的印象部分关于成像研究是放射科医生的推理和结论的摘要，它还有助于参考医师确认或排除某些诊断。需要一系列任务才能自动生成典型信息富裕放射学报告的抽象性摘要。这些任务包括从报告中获取显着内容以及生成简洁，易于消耗的印象部分。关于放射学报告摘要的先前研究集中在单步端到端模型上，其中包含了显着内容获取的任务。为了充分探索放射学报告摘要的级联结构和解释性，我们介绍了两项创新。首先，我们设计了一种两步方法：提取性摘要，然后是抽象性摘要。其次，我们还将提取部分分解为两个独立的任务：突出（1）句子和（2）关键字的提取。来自两个临床部位的英语放射学报告的实验表明，与单步相比，我们的新方法可以提出更精确的摘要，并获得了两步 - 带有single-single-aftrective-Process-Process-Process基线，而F1得分的总体提高了3-4％。
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%.
