Ontology-Aware Clinical Abstractive Summarization
This work addresses the need for efficient and error-reducing clinical summarization for clinicians, though it appears incremental as it builds on existing methods with domain-specific enhancements.
The authors tackled the problem of generating accurate summaries from clinical reports by proposing an ontology-augmented sequence-to-sequence model, which significantly outperformed the state-of-the-art on radiology reports in terms of ROUGE scores and reduced omissions of important details in human evaluations.
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.