Attend to Medical Ontologies: Content Selection for Clinical Abstractive Summarization
Table 3: Rouge results showing the impact of content selector in summarization model. ∗ shows the statistical significance (paired t-test, p<0.05).
['Setting', 'RG-1', 'RG-2', 'RG-L']
As expected, BUS approach achieves the best results among the baseline models by constraining decoder’s attention over odds-on-copied terms, but still underperforms our model. This may suggest that the intermediate stage of refining word representations based on the ontological word would lead to a better performance than superficially restricting attention over the salient terms. Table. For the setting without content selector, we encode all ontologies within the Findings. As shown, our model statistically significantly improves the results on RG-1 and RG-2.