FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization

by   Qianqian Xie, et al.

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.


page 1

page 2

page 3

page 4


How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing

Under special circumstances, summaries should conform to a particular st...

Towards Summary Candidates Fusion

Sequence-to-sequence deep neural models fine-tuned for abstractive summa...

Faithfulness-Aware Decoding Strategies for Abstractive Summarization

Despite significant progress in understanding and improving faithfulness...

Improving Factual Consistency of Abstractive Summarization via Question Answering

A commonly observed problem with the state-of-the art abstractive summar...

Towards objectively evaluating the quality of generated medical summaries

We propose a method for evaluating the quality of generated text by aski...

Leveraging Summary Guidance on Medical Report Summarization

This study presents three deidentified large medical text datasets, name...

Generating EDU Extracts for Plan-Guided Summary Re-Ranking

Two-step approaches, in which summary candidates are generated-then-rera...

Please sign up or login with your details

Forgot password? Click here to reset