LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization

Tian Shi, Ping Wang, Chandan K. Reddy


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.
Anthology ID:
N19-4012
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
66–71
Language:
URL:
https://aclanthology.org/N19-4012
DOI:
10.18653/v1/N19-4012
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/N19-4012.pdf