Neural Headline Generation with Self-Training
Computation Journalism 2019 (CJ2019), 2019/2
Natural Language Processing
- In this paper we propose a novel method which incorporates self- training into a sequence-to-sequence model in order to improve the accuracy of the headline generation task. Our baseline model is based on neural network-based sequence-to-sequence learning with an attention mechanism and trained with approximately 100,000 labeled examples and 2,000,000 unlabeled examples. Through ex- periments, we show our proposal signi cantly improves the accu- racy and works e ectively.
Neural Headline Generation with Self-Training（External Site Link）