Controlling the Amount of Verbatim Copying in Abstractive Summarization
Table 5: Summarization results on the Newsroom test set. The top four systems are trained on Newsroom training data, whereas the bottom two systems are trained on Gigaword.
['[EMPTY]', '[BOLD] System', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L', 'Bert-S']
Domain adaptation We investigate the effect of domain adaptation by training the model on Gigaword then testing it on Newsroom test set. Not surprisingly, there is a performance degradation when testing the model in a cross-domain setting. We observe that the model with more copying (pure-extract, case e.) seem to degrade more gracefully than its counterpart (best-abstract, case f.), with a smaller performance gap in cross-domain settings. Both of our models perform competitively comparing to other baseline methods.