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Recent neural sequence-to-sequence models with a copy mechanism have achievedremarkable progress in various text generation tasks. These models addressedout-of-vocabulary problems and facilitated the generation of rare words.However, the identification of the word which needs to be copied is difficult,as observed by prior copy models, which suffer from incorrect generation andlacking abstractness. In this paper, we propose a novel supervised approach ofa copy network that helps the model decide which words need to be copied andwhich need to be generated. Specifically, we re-define the objective function,which leverages source sequences and target vocabularies as guidance forcopying. The experimental results on data-to-text generation and abstractivesummarization tasks verify that our approach enhances the copying quality andimproves the degree of abstractness.

May the Force Be with Your Copy Mechanism: Enhanced Supervised-Copy Method for Natural Language Generation