# Ontology-Aware Clinical Abstractive Summarization

## TL;DR

This paper introduces an ontology-aware abstractive summarization model for clinical reports, significantly improving summary quality and coverage by integrating domain-specific ontological information, as validated on radiology report datasets.

## Contribution

The paper presents a novel sequence-to-sequence model that incorporates ontological data to enhance clinical report summarization, outperforming existing methods in both automatic and human evaluations.

## Key findings

- Significant improvement in rouge scores over state-of-the-art methods
- Summaries are less likely to omit important clinical details
- Human evaluation confirms better coverage and accuracy

## Abstract

Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive summarization model augmented with domain-specific ontological information to enhance content selection and summary generation. We apply our method to a dataset of radiology reports and show that it significantly outperforms the current state-of-the-art on this task in terms of rouge scores. Extensive human evaluation conducted by a radiologist further indicates that this approach yields summaries that are less likely to omit important details, without sacrificing readability or accuracy.