Learning to Summarize Radiology Findings
Table 1: Main results on the test set of the Stanford reports. “⊕ Background” represents prepending the background section to the findings section to form the input to the model. All the ROUGE scores have a 95% confidence interval of at most ±0.50 as calculated by the official ROUGE script.
['System', 'ROUGE-1', 'ROUGE-2', 'ROUGE-L']
We find that the two non-neural extractive models perform comparably, and both are able to obtain non-trivial subsequence overlap with the reference summaries as measured by ROUGE scores. However, a baseline neural pointer-generator that combines the sequence generation and the copy mechanism beats the non-neural baselines substantially on all metrics. We confirm that naively incorporating the study background information by prepending the background section directly to the input findings in the pointer-generator model in fact hurts the performance (noted by ⊕ Background). In comparison, our model benefits from using the separately encoded background vector to guide the decoding process, and achieves best scores on all ROUGE metrics.