  Copying mechanism shows effectiveness in sequence-to-sequence based neural
network models for text generation tasks, such as abstractive sentence
summarization and question generation. However, existing works on modeling
copying or pointing mechanism only considers single word copying from the
source sentences. In this paper, we propose a novel copying framework, named
Sequential Copying Networks (SeqCopyNet), which not only learns to copy single
words, but also copies sequences from the input sentence. It leverages the
pointer networks to explicitly select a sub-span from the source side to target
side, and integrates this sequential copying mechanism to the generation
process in the encoder-decoder paradigm. Experiments on abstractive sentence
summarization and question generation tasks show that the proposed SeqCopyNet
can copy meaningful spans and outperforms the baseline models.
