@inproceedings{perez-beltrachini-etal-2019-generating,
    title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders",
    author = "Perez-Beltrachini, Laura  and
      Liu, Yang  and
      Lapata, Mirella",
    editor = "Korhonen, Anna  and
      Traum, David  and
      M{\`a}rquez, Llu{\'i}s",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/P19-1504/",
    doi = "10.18653/v1/P19-1504",
    pages = "5107--5116",
    abstract = "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."
}