  In neural abstractive summarization, the conventional sequence-to-sequence
(seq2seq) model often suffers from repetition and semantic irrelevance. To
tackle the problem, we propose a global encoding framework, which controls the
information flow from the encoder to the decoder based on the global
information of the source context. It consists of a convolutional gated unit to
perform global encoding to improve the representations of the source-side
information. Evaluations on the LCSTS and the English Gigaword both demonstrate
that our model outperforms the baseline models, and the analysis shows that our
model is capable of reducing repetition.
