Automatic radiology report summarization is a crucial clinical task, whose key challenge is to maintain factual accuracy between produced summaries and ground truth radiology findings. Existing research adopts reinforcement learning to directly optimize factual consistency metrics such as CheXBert or RadGraph score. However, their decoding method using greedy search or beam search considers no factual consistency when picking the optimal candidate, leading to limited factual consistency improvement. To address it, we propose a novel second-stage summarizing approach FactReranker, the first attempt that learns to choose the best summary from all candidates based on their estimated factual consistency score. We propose to extract medical facts of the input medical report, its gold summary, and candidate summaries based on the RadGraph schema and design the fact-guided reranker to efficiently incorporate the extracted medical facts for selecting the optimal summary. We decompose the fact-guided reranker into the factual knowledge graph generation and the factual scorer, which allows the reranker to model the mapping between the medical facts of the input text and its gold summary, thus can select the optimal summary even the gold summary can't be observed during inference. We also present a fact-based ranking metric (RadMRR) for measuring the ability of the reranker on selecting factual consistent candidates. Experimental results on two benchmark datasets demonstrate the superiority of our method in generating summaries with higher factual consistency scores when compared with existing methods.

翻译：自动放射学报告摘要是关键的临床任务，其核心挑战在于确保生成的摘要与真实放射学发现之间的事实准确性。现有研究采用强化学习直接优化事实一致性指标（如CheXBert或RadGraph分数）。然而，其基于贪婪搜索或束搜索的解码方法在选择最优候选时未考虑事实一致性，导致事实一致性的提升有限。为此，我们提出了一种新颖的二阶段摘要方法FactReranker，这是首次尝试依据候选摘要估计的事实一致性得分，从所有候选中学习选择最佳摘要。我们基于RadGraph架构提取输入医疗报告、金标准摘要及候选摘要的医学事实，并设计事实引导的重排序器，有效整合提取的医学事实以选择最优摘要。我们将事实引导重排序器分解为事实知识图谱生成与事实评分器，使其能够建模输入文本医学事实与金标准摘要之间的映射关系，从而在推理阶段即使无法观察金标准摘要时，仍能选择最优摘要。我们还提出基于事实的排序指标（RadMRR），用于衡量重排序器选择事实一致性候选的能力。在两个基准数据集上的实验结果表明，与现有方法相比，我们的方法在生成具有更高事实一致性得分的摘要方面具有优越性。