  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.
