  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.
