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.
@article{arxiv.1906.01512,
title = {LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization},
author = {Tian Shi and Ping Wang and Chandan K. Reddy},
journal= {arXiv preprint arXiv:1906.01512},
year = {2019}
}