Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (8): 132-137.

### Chinese Summarization Research on Combination of Local Attention and Convolutional Neural Network

ZHOU Caidong1, ZENG Biqing1，2, WANG Shengyu1, SHANG Qi1

1. 1.School of Computer, South China Normal University, Guangzhou 510631, China
2.School of Software, South China Normal University, Foshan, Guangdong 528225, China
• Online:2019-04-15 Published:2019-04-15

### 结合注意力与卷积神经网络的中文摘要研究

1. 1.华南师范大学 计算机学院，广州 510631
2.华南师范大学 软件学院，广东 佛山 528225

Abstract: At present, deep learning has been widely applied in the field of English text summarization, but it is rarely used in the field of Chinese text summarization. In addition, the model mainly used in the field of text summarization is the encoder-decoder model, inputting original text information in the encoder, lacking use of advanced features of the text, resulting in inadequate encode information, repetition of the generated abstracts, word order disorder and other issues. Therefore, this paper proposes an encoder-decoder model with high-level feature extraction capability that combines local attention with convolutional neural network. The model extracts the advanced features of the text by means of combining the local attention mechanism and the convolutional neural network, which are used as the input of the encoder, and then the summary is generated by the decoder based on the global attention mechanism. Experiments on Chinese text datasets prove that this model has good performance in Chinese summarization.