  Copy mechanism allows sequence-to-sequence models to choose words from the
input and put them directly into the output, which is finding increasing use in
abstractive summarization. However, since there is no explicit delimiter in
Chinese sentences, most existing models for Chinese abstractive summarization
can only perform character copy, resulting in inefficient. To solve this
problem, we propose a lexicon-constrained copying network that models
multi-granularity in both encoder and decoder. On the source side, words and
characters are aggregated into the same input memory using a Transformerbased
encoder. On the target side, the decoder can copy either a character or a
multi-character word at each time step, and the decoding process is guided by a
word-enhanced search algorithm that facilitates the parallel computation and
encourages the model to copy more words. Moreover, we adopt a word selector to
integrate keyword information. Experiments results on a Chinese social media
dataset show that our model can work standalone or with the word selector. Both
forms can outperform previous character-based models and achieve competitive
performances.
