A Hierarchical Neural Abstractive Summarization with Self-Attention Mechanism

DOI

10.2991/amcce-18.2018.89How to use a DOI?

Keywords

Neural Abstractive Summarization, Self-Attention Mechanism

Abstract

Recently, the attentional seq2seq model had made a remarkable progress on the abstractive summarization. But most of these models do not considers the relation between original sentences, which is the important feature in extractive method. In this work, we proposed a Hierarchical Neural model to address problem. First, we use a self-attention to discovers the relation between original sentences. Secondly, we use a copy mechanism to solve the OOV problem. The experiment demonstrates that our model achieves state-of-the-art ROUGE scores on LCSTS dataset.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - WeiJun Yang
AU  - ZhiCheng Tang
AU  - XinHuai Tang
PY  - 2018/05
DA  - 2018/05
TI  - A Hierarchical Neural Abstractive Summarization with Self-Attention Mechanism
BT  - Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018)
PB  - Atlantis Press
SP  - 514
EP  - 518
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-18.2018.89
DO  - 10.2991/amcce-18.2018.89
ID  - Yang2018/05
ER  -