Learning to Summarize Radiology Findings
Table 5: Error analysis on 100 sampled dev examples from the Stanford dataset.
['Category', 'Percentage']
We find that 63% examples are qualitatively close to the reference summary, which aligns well with the radiologist evaluation result. Among the four error categories, missing critical information is the most common error with 24% examples, suggesting that the summaries may be improved with explicit modeling of the importance of different radiology findings. We also find through qualitative analysis that the model tends to miss on followup procedures recommended by the human radiologist, since these procedures are often not included in the findings section and generating them needs significant understanding of the study and domain knowledge.