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arXiv · 1905.05818

Ontology-Aware Clinical Abstractive Summarization

Sean MacAvaney·Sajad Sotudeh·Arman Cohan·Nazli Goharian·Ish Talati·Ross W. Filice
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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.

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Sean MacAvaney, Sajad Sotudeh, Arman Cohan, Nazli Goharian, Ish Talati, Ross W. Filice. 2019-05-14. Ontology-Aware Clinical Abstractive Summarization. https://arxiv.org/abs/1905.05818

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