ArXiv Preprint
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.
Qianqian Xie, Jinpeng Hu, Jiayu Zhou, Yifan Peng, Fei Wang
2023-03-15
