Structure-Infused Copy Mechanisms for Abstractive Summarization
Table 11: Effects of applying the coverage regularizer and the reference beam search to structural models, evaluated on test-1951. Combining both yields the highest scores.
['| [ITALIC] V|', '[BOLD] R-2', '[BOLD] Train Speed', '[BOLD] InVcb', '[BOLD] InVcb+Src']
Coverage and reference beam. The coverage regularizer is applied in a second training stage, where the system is trained for an extra 5 epochs with coverage and the model yielding the lowest validation loss is selected. Both coverage and ref_beam can improve the system performance. Our observation suggests that ref_beam is an effective addition to shorten the gap between different systems. Output vocabulary size. All our models by default use an output vocabulary of 5K words in order to make the results comparable to state-of-the-art-systems. However, we observe that there is a potential to further boost the system performance (17.25→17.62 R-2 F1-score, w/o coverage or ref_beam) if we had chosen to use a larger vocabulary (10K) and can endure a slightly longer training time (1.2x). The gap between the two conditions shortens as the size of the output vocabulary is increased.