# Comparative Analysis of Abstractive Summarization Models for Clinical Radiology Reports

arXiv: https://arxiv.org/abs/2506.16247 · HTML: https://mlsanity.com/paper/2506.16247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 19 Jun 2025.

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

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [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)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT](https://mlsanity.com/paper/2304.08448/) (2023)
- [Graph Enhanced Contrastive Learning for Radiology Findings Summarization](https://mlsanity.com/paper/2204.00203/) (2022)
- [Contrastive Attention Mechanism for Abstractive Sentence Summarization](https://mlsanity.com/paper/1910.13114/) (2019)
- [Generation of Highlights from Research Papers Using Pointer-Generator Networks and SciBERT Embeddings](https://mlsanity.com/paper/2302.07729/) (2023)
- [SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization](https://mlsanity.com/paper/2303.13035/) (2023)
- [Named Entity Recognition Based Automatic Generation of Research Highlights](https://mlsanity.com/paper/2303.12795/) (2023)
- [An Analysis of Abstractive Text Summarization Using Pre-trained Models](https://mlsanity.com/paper/2303.12796/) (2023)
- [A Survey for Biomedical Text Summarization: From Pre-trained to Large Language Models](https://mlsanity.com/paper/2304.08763/) (2023)
- [Abstractive Text Summarization using Attentive GRU based Encoder-Decoder](https://mlsanity.com/paper/2302.13117/) (2023)
- [Analysis of Multidomain Abstractive Summarization Using Salience Allocation](https://mlsanity.com/paper/2402.11955/) (2024)
- [RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization](https://mlsanity.com/paper/2406.03062/) (2024)

## Abstract (arXiv)

The findings section of a radiology report is often detailed and lengthy, whereas the impression section is comparatively more compact and captures key diagnostic conclusions. This research explores the use of advanced abstractive summarization models to generate the concise impression from the findings section of a radiology report. We have used the publicly available MIMIC-CXR dataset. A comparative analysis is conducted on leading pre-trained and open-source large language models, including T5-base, BART-base, PEGASUS-x-base, ChatGPT-4, LLaMA-3-8B, and a custom Pointer Generator Network with a coverage mechanism. To ensure a thorough assessment, multiple evaluation metrics are employed, including ROUGE-1, ROUGE-2, ROUGE-L, METEOR, and BERTScore. By analyzing the performance of these models, this study identifies their respective strengths and limitations in the summarization of medical text. The findings of this paper provide helpful information for medical professionals who need automated summarization solutions in the healthcare sector.