FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization

Understand

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.