Research Area:  Machine Learning

Abstract:

Abstract text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolutional seq2seq architecture. A hierarchical CNN framework is much more efficient than the conventional RNN seq2seq models. We also equip our model with a copying mechanism to deal with the rare or unseen words. Additionally, we incorporate a hierarchical attention mechanism to model the keywords and key sentences simultaneously. Finally we verify our model on two real-life datasets, GigaWord and DUC corpus. The experiment results verify the effectiveness of our model as it outperforms state-of-the-art alternatives consistently and statistical significantly.

Keywords:  

Author(s) Name:  Yong Zhang ,Dan Li ,Yuheng Wang,Yang Fang and Weidong Xiao

Publisher name:  MDPI

DOI:  10.3390/app9081665

Volume Information:  Volume 9 Issue 8 10.3390/app9081665

Paper Link:   https://www.mdpi.com/2076-3417/9/8/1665