JSAI2018

Presentation information

Poster presentation

General Session » Interactive

[3Pin1] インタラクティブ(1)

Thu. Jun 7, 2018 9:00 AM - 10:40 AM Room P (4F Emerald Lobby)

9:00 AM - 10:40 AM

[3Pin1-36] Neural Headline Generation with Self-Training

Shintaro Takemae2, Kazuma Murao1, 〇Taichi Yatsuka1, Hayato Kobayashi1,3, Masaki Noguchi1, Hitoshi Nishikawa2, Takenobu Tokunaga2 (1. Yahoo Japan Corporation, 2. Tokyo Institute of Technology, 3. RIKEN AIP)

Keywords:Summarization, Self-training, Neural Network

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.