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:
Cite (ACL):
Tian Shi, Ping Wang, and Chandan K. Reddy. 2019. LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 66–71, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization (Shi et al., NAACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/N19-4012.pdf
Code
 tshi04/LeafNATS
Data
CNN/Daily MailNEWSROOM