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:自动放射报告汇总是一项至关重要的临床任务,其关键挑战是保持所制作的摘要和地面真象放射调查结果之间的事实准确性。现有研究采用强化学习,直接优化CheXudubert或RadGraph得分等事实一致性指标;然而,其使用贪婪搜索或梁搜索的解码方法认为,在选择最佳候选人时,在选择最佳候选人时,其解码方法并不具有实际一致性,导致在事实一致性方面的改进有限。为了解决这一问题,我们提议了一个新的第二阶段总结方法,即“刀锋”,这是根据估计的事实一致性得分从所有候选人中选择最佳摘要的第一个尝试。我们提议根据RadGraph schema,从输入的医疗报告、其黄金摘要和候选人摘要中提取医学事实事实事实事实事实,并设计事实引导重置器,以便有效地将提取的医疗事实事实事实事实事实事实事实数据纳入最佳摘要中。我们将事实引导的重新排序纳入事实知识图表的生成和事实分数中,使重新排序者能够根据对输入文本的医学事实事实事实和黄金摘要进行模拟,因此,我们提议最佳摘要的提要选择最佳摘要,在评分期间可以观察到黄金摘要,并用不断的比正标的方法来衡量候选人的比标。我们目前的实际结果。</s>