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.
@article{arxiv.1905.05818,
title = {Ontology-Aware Clinical Abstractive Summarization},
author = {Sean MacAvaney and Sajad Sotudeh and Arman Cohan and Nazli Goharian and Ish Talati and Ross W. Filice},
journal= {arXiv preprint arXiv:1905.05818},
year = {2019}
}