Neural Headline Generation with Self-Training

Abstract

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 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 experiments, we show our proposal significantly improves the accuracy and works effectively.