LeafNATS: An Open-Source Toolkit and Live Demo System for Neural Abstractive Text Summarization
arXiv:1906.01512
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.
arXiv commentAccepted by NAACL-HLT 2019 demo track
References in corpus (9)
- Distributed Representations of Words and Phrases and their Compositionality
- Sequence to Sequence Learning with Neural Networks
- Teaching Machines to Read and Comprehend
- A Deep Reinforced Model for Abstractive Summarization
- Sequence Level Training with Recurrent Neural Networks
- SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
- Neural Abstractive Text Summarization with Sequence-to-Sequence Models
- XNMT: The eXtensible Neural Machine Translation Toolkit
- Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation