Heterogeneous Graph Neural Networks for Extractive Document Summarization
Table 5: Different turns of iterative updating of sentence nodes. The experiments are performed on the validation set of CNN/DM. Time is the average time of one epoch.
['[BOLD] Number', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L', '[BOLD] Time']
In order to select the best iteration number for HeterSUMGraph, we compare performances of different t on the validation set of CNN/DM. All models are trained on a single GeForce RTX 2080 Ti GPU for about 5 epochs. However, when the iteration number goes from 1 to 3, the time for one epoch nearly doubles. Therefore, we take t=1 as a result of the balance of time cost and model performance.