mlsanityData to 14 Oct 2026

GersteinLab at MEDIQA-Chat 2023: Clinical Note Summarization from Doctor-Patient Conversations through Fine-tuning and In-context Learning

arXiv 2305.05001 ↗First posted 8 May 2023cs.CLcited by 9

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Abstract (arXiv)

This paper presents our contribution to the MEDIQA-2023 Dialogue2Note shared task, encompassing both subtask A and subtask B. We approach the task as a dialogue summarization problem and implement two distinct pipelines: (a) a fine-tuning of a pre-trained dialogue summarization model and GPT-3, and (b) few-shot in-context learning (ICL) using a large language model, GPT-4. Both methods achieve excellent results in terms of ROUGE-1 F1, BERTScore F1 (deberta-xlarge-mnli), and BLEURT, with scores of 0.4011, 0.7058, and 0.5421, respectively. Additionally, we predict the associated section headers using RoBERTa and SciBERT based classification models. Our team ranked fourth among all teams, while each team is allowed to submit three runs as part of their submission. We also utilize expert annotations to demonstrate that the notes generated through the ICL GPT-4 are better than all other baselines. The code for our submission is available.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 17 of its 27 references that are in the map. First posted on arXiv 8 May 2023.

Builds on

17 of 27 references.

References · 17

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  5. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  6. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,414
  7. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  8. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
  9. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  10. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  11. 2019ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmissioncited by 493
  12. 2022News Summarization and Evaluation in the Era of GPT-3cited by 327
5 more
  1. 2020Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniquescited by 64
  2. 2019Enhancing Clinical Concept Extraction with Contextual Embeddingscited by 48
  3. 2021CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuningcited by 44
  4. 2021Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summariescited by 15
  5. 2022A Survey on Medical Document Summarizationcited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 1.

9 papers.

  1. 2023MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoningcited by 316
  2. 2023GPT4GEO: How a Language Model Sees the World's Geographycited by 68
  3. 2024Exploring Robustness in Doctor-Patient Conversation Summarization: An Analysis of Out-of-Domain SOAP Notescited by 7
  4. 2024Improving Clinical Note Generation from Complex Doctor-Patient Conversationcited by 7
  5. 2025CLI-RAG: A Retrieval-Augmented Framework for Clinically Structured and Context Aware Text Generation with LLMscited by 4
  6. 2025Natural Language Generation in Healthcare: A Review of Methods and Applicationscited by 3
  7. 2024Leveraging language models for summarizing mental state examinations: A comprehensive evaluation and dataset releasecited by 2
  8. 2026From Triage to Discharge: A Survey of NLP Tasks, Methods, and Open Challenges in the Emergency Departmentcited by 1
  9. 2025Classification of Psychiatry Clinical Notes by Diagnosis: A Deep Learning and Machine Learning Approachcited by 0