Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
Table 1: Rouge results on MIMIC-CXR. ∗ shows the statistical significance (paired t-test, p<0.05).
['Method', 'RG-1', 'RG-2', 'RG-L']
Table. Our model significantly outperforms all the baselines on all Rouge metrics with 2.9%, 2.5%, and 1.9% improvements for RG-1, RG-2, and RG-L, respectively. While NeuSum outperforms the non-neural LSA in extractive setting, the extractive models lag behind the abstractive methods considerably, suggesting that human-written impressions are formed by abstractively selecting information from the findings, not merely extracting source sentences. When comparing Ont. PG with our model , it turns out that indeed our hypothesis is valid that a pre-step of identifying significant ontological terms can improve the summary generation substantially. As pointed out earlier, we define the saliency of an ontological term by its copying probability.