Abstract Meaning Representation for Hospital Discharge Summarization
Journal:
arXiv
Published Date:
Jun 17, 2025
Abstract
The Achilles heel of Large Language Models (LLMs) is hallucination, which has
drastic consequences for the clinical domain. This is particularly important
with regards to automatically generating discharge summaries (a lengthy medical
document that summarizes a hospital in-patient visit). Automatically generating
these summaries would free physicians to care for patients and reduce
documentation burden. The goal of this work is to discover new methods that
combine language-based graphs and deep learning models to address provenance of
content and trustworthiness in automatic summarization. Our method shows
impressive reliability results on the publicly available Medical Information
Mart for Intensive III (MIMIC-III) corpus and clinical notes written by
physicians at Anonymous Hospital. rovide our method, generated discharge ary
output examples, source code and trained models.
