Towards Understanding of Medical Randomized Controlled Trialsby Conclusion Generation
Table 3: Human evaluation results for text understanding on the annotation questions of 50 randomly selected source documents. Note that some source documents which don’t fit into the binary paradigm of positive or negative results are classified as N/A. TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative; N/A: Not Applicable.
['[BOLD] System', '[BOLD] TP', '[BOLD] TN', '[BOLD] FP', '[BOLD] FN', '[BOLD] N/A', '[BOLD] Acc.']
It is clear that the proposed GPT-2 achieves better performance in terms of accuracy (the ratio of true samples). We also include the performance of human-written conclusions in the last row, which serves as the upper bound of this task. However, there is still a gap between human-written conclusions and the generated ones.