# Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization

arXiv: https://arxiv.org/abs/2203.08257 · HTML: https://mlsanity.com/paper/2203.08257/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 15 Mar 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Learning Multiagent Communication with Backpropagation](https://mlsanity.com/paper/1605.07736/) (2016)
- [Emergence of Grounded Compositional Language in Multi-Agent Populations](https://mlsanity.com/paper/1703.04908/) (2017)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [Automated Phrase Mining from Massive Text Corpora](https://mlsanity.com/paper/1702.04457/) (2017)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss](https://mlsanity.com/paper/1805.06266/) (2018)
- [Learning to Communicate Implicitly By Actions](https://mlsanity.com/paper/1810.04444/) (2018)

## Built on by (most cited first, 13 of 13)

- [An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT](https://mlsanity.com/paper/2304.08448/) (2023)
- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [Toward expanding the scope of radiology report summarization to multiple anatomies and modalities](https://mlsanity.com/paper/2211.08584/) (2022)
- [shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation](https://mlsanity.com/paper/2306.03264/) (2023)
- [RadLing: Towards Efficient Radiology Report Understanding](https://mlsanity.com/paper/2306.02492/) (2023)
- [Fusion of Domain-Adapted Vision and Language Models for Medical Visual Question Answering](https://mlsanity.com/paper/2404.16192/) (2024)
- [Improving Radiology Summarization with Radiograph and Anatomy Prompts](https://mlsanity.com/paper/2210.08303/) (2022)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play](https://mlsanity.com/paper/2410.18935/) (2024)
- [KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization](https://mlsanity.com/paper/2307.07409/) (2023)
- [Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis](https://mlsanity.com/paper/2307.12803/) (2023)
- [CSTRL: Context-Driven Sequential Transfer Learning for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2503.05750/) (2025)
- [Summarization of Investment Reports Using Pre-trained Model](https://mlsanity.com/paper/2408.01744/) (2024)

## Abstract (arXiv)

The IMPRESSIONS section of a radiology report about an imaging study is a summary of the radiologist's reasoning and conclusions, and it also aids the referring physician in confirming or excluding certain diagnoses. A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report. These tasks include acquisition of salient content from the report and generation of a concise, easily consumable IMPRESSIONS section. Prior research on radiology report summarization has focused on single-step end-to-end models -- which subsume the task of salient content acquisition. To fully explore the cascade structure and explainability of radiology report summarization, we introduce two innovations. First, we design a two-step approach: extractive summarization followed by abstractive summarization. Second, we additionally break down the extractive part into two independent tasks: extraction of salient (1) sentences and (2) keywords. Experiments on English radiology reports from two clinical sites show our novel approach leads to a more precise summary compared to single-step and to two-step-with-single-extractive-process baselines with an overall improvement in F1 score Of 3-4%.