@inproceedings{shi-etal-2019-leafnats,
    title = "{L}eaf{NATS}: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization",
    author = "Shi, Tian  and
      Wang, Ping  and
      Reddy, Chandan K.",
    editor = "Ammar, Waleed  and
      Louis, Annie  and
      Mostafazadeh, Nasrin",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-4012/",
    doi = "10.18653/v1/N19-4012",
    pages = "66--71",
    abstract = "Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog/news editors by providing them suggestions of headlines and summaries of their articles."
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    <abstract>Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog/news editors by providing them suggestions of headlines and summaries of their articles.</abstract>
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%0 Conference Proceedings
%T LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
%A Shi, Tian
%A Wang, Ping
%A Reddy, Chandan K.
%Y Ammar, Waleed
%Y Louis, Annie
%Y Mostafazadeh, Nasrin
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F shi-etal-2019-leafnats
%X Neural abstractive text summarization (NATS) has received a lot of attention in the past few years from both industry and academia. In this paper, we introduce an open-source toolkit, namely LeafNATS, for training and evaluation of different sequence-to-sequence based models for the NATS task, and for deploying the pre-trained models to real-world applications. The toolkit is modularized and extensible in addition to maintaining competitive performance in the NATS task. A live news blogging system has also been implemented to demonstrate how these models can aid blog/news editors by providing them suggestions of headlines and summaries of their articles.
%R 10.18653/v1/N19-4012
%U https://aclanthology.org/N19-4012/
%U https://doi.org/10.18653/v1/N19-4012
%P 66-71
Markdown (Informal)
[LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization](https://aclanthology.org/N19-4012/) (Shi et al., NAACL 2019)
ACL