  Existing neural generation approaches create multi-sentence text as a single
sequence. In this paper we propose a structured convolutional decoder that is
guided by the content structure of target summaries. We compare our model with
existing sequential decoders on three data sets representing different domains.
Automatic and human evaluation demonstrate that our summaries have better
content coverage.
