Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation
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Computer Science > Computation and Language
arXiv:2609.13581 (cs)
[Submitted on 11 Sep 2026]
Title:Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation
View a PDF of the paper titled Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation, by Paul Landes and 3 other authors
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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.
| Comments: | Published in the 2026 IEEE 14th International Conference on Healthcare Informatics (ICHI). 10 pages, 6 figures |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2609.13581 [cs.CL] |
| (or arXiv:2609.13581v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.13581
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | 2026 IEEE 14th International Conference on Healthcare Informatics (ICHI), pp. 93-102, 2026 |
| Related DOI: | https://doi.org/10.1109/ICHI69079.2026.00023
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View a PDF of the paper titled Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation, by Paul Landes and 3 other authors
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