Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation

arXiv:2609.13581v1 Announce Type: new 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.

Related

• Less is More: Quality-Aware Training Data Selection for Scientific Summarization
• Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content
• Mix and Match: Context Pairing for Scalable Topic-Controlled Educational Summarisation
• IDEAL: Leveraging Infinite and Dynamic Characterizations of Large Language Models for Query-focused Summarization

Source: arXiv cs.CL | 2026-09-15