Toward Complete Hospital Discharge Summarization with Abstract Meaning Representation

A new arXiv paper (2609.13581v1) presents an evidence-driven alignment framework for hospital discharge summarization that treats provenance as a first-class constraint, using semantic graphs and deep learning models to select and organize each summary sentence via cross-document semantic alignment with explicit evidence links to source spans. The authors report results on two corpora: the publicly available MIMIC-III corpus and clinical notes written by physicians at the University of Illinois Hospital (UIC Health), and they make source code and trained models available. The work targets the hallucination risk that the authors identify as the primary obstacle to using large language models for clinical documentation.

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.