# Towards Clinical Encounter Summarization: Learning to Compose Discharge Summaries from Prior Notes

arXiv: https://arxiv.org/abs/2104.13498 · HTML: https://mlsanity.com/paper/2104.13498/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 27 Apr 2021.

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

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Asking and Answering Questions to Evaluate the Factual Consistency of Summaries](https://mlsanity.com/paper/2004.04228/) (2020)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://mlsanity.com/paper/1805.11080/) (2018)
- [Faithful to the Original: Fact Aware Neural Abstractive Summarization](https://mlsanity.com/paper/1711.04434/) (2017)
- [Hierarchical Transformers for Multi-Document Summarization](https://mlsanity.com/paper/1905.13164/) (2019)
- [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://mlsanity.com/paper/1911.02541/) (2019)
- [Learning to Summarize Radiology Findings](https://mlsanity.com/paper/1809.04698/) (2018)
- [Ontology-Aware Clinical Abstractive Summarization](https://mlsanity.com/paper/1905.05818/) (2019)
- [Revisiting Summarization Evaluation for Scientific Articles](https://mlsanity.com/paper/1604.00400/) (2016)
- [Natural Language Generation for Electronic Health Records](https://mlsanity.com/paper/1806.01353/) (2018)
- [Guiding Extractive Summarization with Question-Answering Rewards](https://mlsanity.com/paper/1904.02321/) (2019)

## Built on by (most cited first, 19 of 19)

- [Learning to Revise References for Faithful Summarization](https://mlsanity.com/paper/2204.10290/) (2022)
- [Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials](https://mlsanity.com/paper/2409.04481/) (2024)
- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [Enhancing Clinical Efficiency through LLM: Discharge Note Generation for Cardiac Patients](https://mlsanity.com/paper/2404.05144/) (2024)
- [Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study](https://mlsanity.com/paper/2402.19052/) (2024)
- [FaMeSumm: Investigating and Improving Faithfulness of Medical Summarization](https://mlsanity.com/paper/2311.02271/) (2023)
- [Leveraging Summary Guidance on Medical Report Summarization](https://mlsanity.com/paper/2302.04001/) (2023)
- [A Method to Automate the Discharge Summary Hospital Course for Neurology Patients](https://mlsanity.com/paper/2305.06416/) (2023)
- [SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrieval](https://mlsanity.com/paper/2401.02369/) (2024)
- [Toward Reliable Clinical Coding with Language Models: Verification and Lightweight Adaptation](https://mlsanity.com/paper/2510.07629/) (2025)
- [Exploring Optimal Granularity for Extractive Summarization of Unstructured Health Records: Analysis of the Largest Multi-Institutional Archive of Health Records in Japan](https://mlsanity.com/paper/2209.10041/) (2022)
- [Neural Summarization of Electronic Health Records](https://mlsanity.com/paper/2305.15222/) (2023)
- [Large Language Model-Based Generation of Discharge Summaries](https://mlsanity.com/paper/2512.06812/) (2025)
- [Is In-hospital Meta-information Useful for Abstractive Discharge Summary Generation?](https://mlsanity.com/paper/2303.06002/) (2023)
- [Query-Guided Self-Supervised Summarization of Nursing Notes](https://mlsanity.com/paper/2407.04125/) (2024)
- [LCDS: A Logic-Controlled Discharge Summary Generation System Supporting Source Attribution and Expert Review](https://mlsanity.com/paper/2507.05319/) (2025)
- [Graph Alignment Topology as an Inductive Bias for Grounding Detection](https://mlsanity.com/paper/2605.22963/) (2026)
- [Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation](https://mlsanity.com/paper/2609.13581/) (2026)
- [Hidden Data Challenges in Clinical Machine Learning](https://mlsanity.com/paper/2610.13350/) (2026)

## Abstract (arXiv)

The records of a clinical encounter can be extensive and complex, thus placing a premium on tools that can extract and summarize relevant information. This paper introduces the task of generating discharge summaries for a clinical encounter. Summaries in this setting need to be faithful, traceable, and scale to multiple long documents, motivating the use of extract-then-abstract summarization cascades. We introduce two new measures, faithfulness and hallucination rate for evaluation in this task, which complement existing measures for fluency and informativeness. Results across seven medical sections and five models show that a summarization architecture that supports traceability yields promising results, and that a sentence-rewriting approach performs consistently on the measure used for faithfulness (faithfulness-adjusted F3) over a diverse range of generated sections.