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2019 NAACL NAACL 2019

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

Abstract

AbstractNeural 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.

🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Tian Shi , Ping Wang , Chandan K. Reddy

Topics

Natural Language Processing > Generation > Summarization Natural Language Processing > Generation > Text Generation

Keywords

text summarization headline generation sequence-to-sequence model abstractive summarization neural network
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