Automatic text summary generation method based on hybrid model DNM
Kuan Feng Xu, Bo Liu, Jianqiang Li, Yong Li, Chen Hl, Guangzhi Qu · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
With the rapid increase of text data generated by the Internet, the problem of text information overload is becoming more and more serious. Automatic text summarization provides a good method to simplify text information. Traditional methods are mainly divided into extractive and abstractive methods. However most extractive methods do not have too much context connection, which leads to unsmooth abstracts. Abstractive method is the mainstream method, but it also deviates from the text content and has the problem of poor readability. In this paper, a hybrid automatic text summarization method is proposed based on deep learning and a rapid self-attention mechanism. This mechanism is used to obtain a hybrid model DNM (Dilated Neural Random Attention with Minimal Risk Loss) by optimizing the network structure and combining it with a specific loss function. The ROUGE score of our model is compared with the extractive Neural Document Summarization (NEUSUM), the abstractive Graph-Based Attentional (GBA) and the hybrid model CopyNet on the LCSTS dataset so as to achieve more accurate and reasonable automatic text summarization.