Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Table 3: Testing performance of LCSTS, where “RNN” is canonical Enc-Dec, and “RNN context” its attentive variant.
['Models', '[EMPTY]', 'ROUGE scores on LCSTS (%) R-1', 'ROUGE scores on LCSTS (%) R-2', 'ROUGE scores on LCSTS (%) R-L']
that CopyNet beats the competitor models with big margin. \newcitehu2015lcsts reports that the performance of a word-based model is inferior to a character-based one. One possible explanation is that a word-based model, even with a much larger vocabulary (50,000 words in \newcitehu2015lcsts), still has a large proportion of OOVs due to the large number of entity names in the summary data and the mistakes in word segmentation. CopyNet, with its ability to handle the OOV words with the copying mechanism, performs however slightly better with the word-based variant.