# Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques

arXiv: https://arxiv.org/abs/2005.01795 · HTML: https://mlsanity.com/paper/2005.01795/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 30 of 64)

- [Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text](https://mlsanity.com/paper/2202.06935/) (2022)
- [ACI-BENCH: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation](https://mlsanity.com/paper/2306.02022/) (2023)
- [SummScreen: A Dataset for Abstractive Screenplay Summarization](https://mlsanity.com/paper/2104.07091/) (2021)
- [Learning to Route LLMs with Confidence Tokens](https://mlsanity.com/paper/2410.13284/) (2024)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [NoteChat: A Dataset of Synthetic Doctor-Patient Conversations Conditioned on Clinical Notes](https://mlsanity.com/paper/2310.15959/) (2023)
- [Human Evaluation and Correlation with Automatic Metrics in Consultation Note Generation](https://mlsanity.com/paper/2204.00447/) (2022)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [Summarizing Patients Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models](https://mlsanity.com/paper/2208.08408/) (2022)
- [BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/2310.19975/) (2023)
- [MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications](https://mlsanity.com/paper/2409.07314/) (2024)
- [MediTOD: An English Dialogue Dataset for Medical History Taking with Comprehensive Annotations](https://mlsanity.com/paper/2410.14204/) (2024)
- [Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency?](https://mlsanity.com/paper/2404.06503/) (2024)
- [Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering](https://mlsanity.com/paper/2206.03886/) (2022)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding](https://mlsanity.com/paper/2204.03035/) (2022)
- [Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions](https://mlsanity.com/paper/2205.13190/) (2022)
- [Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study](https://mlsanity.com/paper/2402.19052/) (2024)
- [Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare](https://mlsanity.com/paper/2405.06784/) (2024)
- [Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients' Active Diagnoses and Problems from Electronic Health Record Progress Notes](https://mlsanity.com/paper/2306.05270/) (2023)
- [Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization](https://mlsanity.com/paper/2305.16548/) (2023)
- [An Investigation of Evaluation Metrics for Automated Medical Note Generation](https://mlsanity.com/paper/2305.17364/) (2023)
- [Abstractive Meeting Summarization: A Survey](https://mlsanity.com/paper/2208.04163/) (2022)
- [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)
- [FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization](https://mlsanity.com/paper/2311.02271/) (2023)
- [What are the Desired Characteristics of Calibration Sets? Identifying Correlates on Long Form Scientific Summarization](https://mlsanity.com/paper/2305.07615/) (2023)
- [MEDSAGE: Enhancing Robustness of Medical Dialogue Summarization to ASR Errors with LLM-generated Synthetic Dialogues](https://mlsanity.com/paper/2408.14418/) (2024)
- [WangLab at MEDIQA-Chat 2023: Clinical Note Generation from Doctor-Patient Conversations using Large Language Models](https://mlsanity.com/paper/2305.02220/) (2023)
- [Improving Summarization with Human Edits](https://mlsanity.com/paper/2310.05857/) (2023)

## Abstract (arXiv)

Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes. While invaluable to clinicians and researchers, creating digital SOAP notes is burdensome, contributing to physician burnout. In this paper, we introduce the first complete pipelines to leverage deep summarization models to generate these notes based on transcripts of conversations between physicians and patients. After exploring a spectrum of methods across the extractive-abstractive spectrum, we propose Cluster2Sent, an algorithm that (i) extracts important utterances relevant to each summary section; (ii) clusters together related utterances; and then (iii) generates one summary sentence per cluster. Cluster2Sent outperforms its purely abstractive counterpart by 8 ROUGE-1 points, and produces significantly more factual and coherent sentences as assessed by expert human evaluators. For reproducibility, we demonstrate similar benefits on the publicly available AMI dataset. Our results speak to the benefits of structuring summaries into sections and annotating supporting evidence when constructing summarization corpora.