# Ontology-Constrained Generation of Domain-Specific Clinical Summaries

arXiv: https://arxiv.org/abs/2411.15666 · HTML: https://mlsanity.com/paper/2411.15666/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2024.

## Built on by (most cited first, 5 of 5)

- [Generative Ontology: When Structured Knowledge Learns to Create](https://mlsanity.com/paper/2602.05636/) (2026)
- [Task-Aware Reduction for Scalable LLM-Database Systems](https://mlsanity.com/paper/2510.11813/) (2025)
- [Dissecting Neuro-Symbolic Quality Assurance for Synthetic Oncology Data Generation](https://mlsanity.com/paper/2608.22085/) (2026)
- [Co-Annotator: Expert-Distilled ViT and VLM for Visual and Documentation Guidance in Age-Related Macular Degeneration](https://mlsanity.com/paper/2608.30352/) (2026)
- [DTScribe: Generating Models for Digital Twin Reporting using Local Large Language Models](https://mlsanity.com/paper/2610.18635/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) offer promising solutions for text summarization. However, some domains require specific information to be available in the summaries. Generating these domain-adapted summaries is still an open challenge. Similarly, hallucinations in generated content is a major drawback of current approaches, preventing their deployment. This study proposes a novel approach that leverages ontologies to create domain-adapted summaries both structured and unstructured. We employ an ontology-guided constrained decoding process to reduce hallucinations while improving relevance. When applied to the medical domain, our method shows potential in summarizing Electronic Health Records (EHRs) across different specialties, allowing doctors to focus on the most relevant information to their domain. Evaluation on the MIMIC-III dataset demonstrates improvements in generating domain-adapted summaries of clinical notes and hallucination reduction.