@inproceedings{xu-etal-2017-decoupling,
    title = "Decoupling Encoder and Decoder Networks for Abstractive Document Summarization",
    author = "Xu, Ying  and
      Lau, Jey Han  and
      Baldwin, Timothy  and
      Cohn, Trevor",
    editor = "Giannakopoulos, George  and
      Lloret, Elena  and
      Conroy, John M.  and
      Steinberger, Josef  and
      Litvak, Marina  and
      Rankel, Peter  and
      Favre, Benoit",
    booktitle = "Proceedings of the {M}ulti{L}ing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-1002",
    doi = "10.18653/v1/W17-1002",
    pages = "7--11",
    abstract = "Abstractive document summarization seeks to automatically generate a summary for a document, based on some abstract {``}understanding{''} of the original document. State-of-the-art techniques traditionally use attentive encoder{--}decoder architectures. However, due to the large number of parameters in these models, they require large training datasets and long training times. In this paper, we propose decoupling the encoder and decoder networks, and training them separately. We encode documents using an unsupervised document encoder, and then feed the document vector to a recurrent neural network decoder. With this decoupled architecture, we decrease the number of parameters in the decoder substantially, and shorten its training time. Experiments show that the decoupled model achieves comparable performance with state-of-the-art models for in-domain documents, but less well for out-of-domain documents.",
}