# FactReranker: Fact-guided Reranker for Faithful Radiology Report Summarization arXiv: https://arxiv.org/abs/2303.08335 ยท HTML: https://mlsanity.com/paper/2303.08335/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 45 references that are in the map. - First posted on arXiv 15 Mar 2023. ## Builds on (most cited first, 30 of 31) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017) - [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020) - [SPICE: Semantic Propositional Image Caption Evaluation](https://mlsanity.com/paper/1607.08822/) (2016) - [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019) - [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019) - [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020) - [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021) - [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://mlsanity.com/paper/1610.02424/) (2016) - [BRIO: Bringing Order to Abstractive Summarization](https://mlsanity.com/paper/2203.16804/) (2022) - [SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization](https://mlsanity.com/paper/2106.01890/) (2021) - [GSum: A General Framework for Guided Neural Abstractive Summarization](https://mlsanity.com/paper/2010.08014/) (2020) - [Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction](https://mlsanity.com/paper/2106.09232/) (2021) - [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019) - [Pre-trained Language Models in Biomedical Domain: A Systematic Survey](https://mlsanity.com/paper/2110.05006/) (2021) - [SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization](https://mlsanity.com/paper/2203.06569/) (2022) - [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018) - [CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning](https://mlsanity.com/paper/1911.10438/) (2019) - [Contrastive Triple Extraction with Generative Transformer](https://mlsanity.com/paper/2009.06207/) (2020) - [Document-level Entity-based Extraction as Template Generation](https://mlsanity.com/paper/2109.04901/) (2021) - [Sequence Level Contrastive Learning for Text Summarization](https://mlsanity.com/paper/2109.03481/) (2021) - [RefSum: Refactoring Neural Summarization](https://mlsanity.com/paper/2104.07210/) (2021) - [A sequence-to-sequence approach for document-level relation extraction](https://mlsanity.com/paper/2204.01098/) (2022) - [Graph Enhanced Contrastive Learning for Radiology Findings Summarization](https://mlsanity.com/paper/2204.00203/) (2022) - [Word Graph Guided Summarization for Radiology Findings](https://mlsanity.com/paper/2112.09925/) (2021) - [MLBiNet: A Cross-Sentence Collective Event Detection Network](https://mlsanity.com/paper/2105.09458/) (2021) - [Alleviating Exposure Bias via Contrastive Learning for Abstractive Text Summarization](https://mlsanity.com/paper/2108.11846/) (2021) - [Toward expanding the scope of radiology report summarization to multiple anatomies and modalities](https://mlsanity.com/paper/2211.08584/) (2022) ## Built on by (most cited first, 6 of 6) - [MEDVOC: Vocabulary Adaptation for Fine-tuning Pre-trained Language Models on Medical Text Summarization](https://mlsanity.com/paper/2405.04163/) (2024) - [shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation](https://mlsanity.com/paper/2306.03264/) (2023) - [A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models](https://mlsanity.com/paper/2402.15422/) (2024) - [A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models](https://mlsanity.com/paper/2304.08763/) (2023) - [Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis](https://mlsanity.com/paper/2307.12803/) (2023) - [M-Eval: A Heterogeneity-Based Framework for Multi-evidence Validation in Medical RAG Systems](https://mlsanity.com/paper/2510.23995/) (2025) ## Abstract (arXiv) 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.