# A Survey on Medical Document Summarization

arXiv: https://arxiv.org/abs/2212.01669 · HTML: https://mlsanity.com/paper/2212.01669/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 61 of its 135 references that are in the map.
- First posted on arXiv 3 Dec 2022.

## Builds on (most cited first, 30 of 61)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [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)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [Re-evaluating Evaluation in Text Summarization](https://mlsanity.com/paper/2010.07100/) (2020)
- [Robustness Gym: Unifying the NLP Evaluation Landscape](https://mlsanity.com/paper/2101.04840/) (2021)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [Representing Numbers in NLP: a Survey and a Vision](https://mlsanity.com/paper/2103.13136/) (2021)
- [MS2: Multi-Document Summarization of Medical Studies](https://mlsanity.com/paper/2104.06486/) (2021)
- [Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings](https://mlsanity.com/paper/2003.11515/) (2020)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Content Selection in Deep Learning Models of Summarization](https://mlsanity.com/paper/1810.12343/) (2018)
- [Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques](https://mlsanity.com/paper/2005.01795/) (2020)
- [Multi-document Summarization via Deep Learning Techniques: A Survey](https://mlsanity.com/paper/2011.04843/) (2020)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [Generating (Factual?) Narrative Summaries of RCTs: Experiments with Neural Multi-Document Summarization](https://mlsanity.com/paper/2008.11293/) (2020)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures](https://mlsanity.com/paper/2009.08666/) (2020)
- [Automated Lay Language Summarization of Biomedical Scientific Reviews](https://mlsanity.com/paper/2012.12573/) (2020)

## Built on by (most cited first, 14 of 14)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [A Survey of Multilingual Reasoning in Language Models](https://mlsanity.com/paper/2502.09457/) (2025)
- [APPLS: Evaluating Evaluation Metrics for Plain Language Summarization](https://mlsanity.com/paper/2305.14341/) (2023)
- [ALLURE: Auditing and Improving LLM-based Evaluation of Text using Iterative In-Context-Learning](https://mlsanity.com/paper/2309.13701/) (2023)
- [SummQA at MEDIQA-Chat 2023:In-Context Learning with GPT-4 for Medical Summarization](https://mlsanity.com/paper/2306.17384/) (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)
- [GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning](https://mlsanity.com/paper/2305.05001/) (2023)
- [Mutual Reinforcement of LLM Dialogue Synthesis and Summarization Capabilities for Few-Shot Dialogue Summarization](https://mlsanity.com/paper/2502.17328/) (2025)
- [CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMs](https://mlsanity.com/paper/2507.06715/) (2025)
- [Enhancing Summarization Performance through Transformer-Based Prompt Engineering in Automated Medical Reporting](https://mlsanity.com/paper/2311.13274/) (2023)
- [Let's Play Across Cultures: A Large Multilingual, Multicultural Benchmark for Assessing Language Models' Understanding of Sports](https://mlsanity.com/paper/2510.01247/) (2025)
- [Leveraging language models for summarizing mental state examinations: A comprehensive evaluation and dataset release](https://mlsanity.com/paper/2403.20145/) (2024)
- [Real-time Speech Summarization for Medical Conversations](https://mlsanity.com/paper/2406.15888/) (2024)
- [M3 QuestionIng: Multi-modal Multi-span Medical Question Answering](https://mlsanity.com/paper/2606.28329/) (2026)

## Abstract (arXiv)

The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for medical studies. As there is so much data accessible to doctors and patients alike, summarizing it has become increasingly necessary - this has been supported through the introduction of deep learning and transformer-based networks, which have boosted the sector significantly in recent years. This paper gives a comprehensive survey of the current techniques and trends in medical summarization