Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation

Abstract:Discharge summaries are lengthy medical documents that summarize a hospital in-patient visit. Automatically generating them can reduce documentation burden and return clinician time to patient care. Whereas Large Language Model (LLMs) could be used for this task, their Achilles heel is hallucinations, which can have drastic consequences for clinical documentation. We present an evidence-driven alignment framework for discharge summarization at the clinical encounter level, that treats provenance as a first-class constraint, using semantic graphs and deep learning models. Each summary sentence is selected and organized via cross-document semantic alignment and is accompanied by explicit evidence links to its source spans. We show our results on two corpora: a publicly available corpus (MIMIC-III) and clinical notes written by physicians at the University of Illinois Hospital (UIC Health). Additionally, we make source code and trained models available.

Published in the 2026 IEEE 14th International Conference on Healthcare Informatics (ICHI). 10 pages, 6 figures

Computation and Language (cs.CL)

arXiv:2609.13581 [cs.CL]

(or arXiv:2609.13581v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.13581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2026 IEEE 14th International Conference on Healthcare Informatics (ICHI), pp. 93-102, 2026

https://doi.org/10.1109/ICHI69079.2026.00023 Focus to learn more DOI(s) linking to related resources

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