Ontology-Aware Clinical Abstractive Summarization
Table 1. rouge results on MedStar Georgetown University Hospital’s development and test sets. Both the UMLS and RadLex ontology PG models are statistically better than the other models (paired t-test, p<0.05).
['Model', 'Development RG-1', 'Development RG-2', 'Development RG-L', 'Test RG-1', 'Test RG-2', 'Test RG-L']
The extractive summarization methods (LexRank and LSA) perform particularly poorly. This may be due to the fact that these approaches are limited to simply selecting sentences from the text, and that the most central sentences may not be the most important for building an effective impression summary. Interestingly, the Back. PG approach (which uses the background section of the report to guide the decoding process) is ineffective on our dataset. This may be due to differences in conventions across institutions, such as what information is included in a report’s background and what is considered important to include in its impression.