关键词: 文本摘要, 神经网络, 注意力机制

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.

Key words: text summary, neural network, attention mechanism