Abstract：Automatic text summarization technology is a method to obtain important information from massive texts,which can alleviate the problem of information overload in the era of big data.The summarization generated by the traditional automatic summarization model based on encoder-decoder is prone to be repeated in sentences and semantic independence,which is not conducive to readers′ understanding of the core ideas of the text.Inspired by the method of manual summarization,the reader first understands the local information of the text,then induces the information and writing summary from the global level.We propose an automatic summarization model based on convolutional self-attention encoding filtering(CSAG).CSAG consists of encoder,gated unit based on convolutional self-attention,decoder.In combination with the convolutional neural network can extracted local features,and the Multi-head self-attention mechanism can learn long-term dependence.The model can extract important feature information from different perspectives and levels according to the local and global features of the context,so as to ensure that the model can generate correct and smooth summarization.Then through policy gradient algorithm for reinforcement learning,the non-differentiable metric ROUGE can be directly used to optimize the model and avoid exposure bias in the testing process.Experimental results on Gigaword dataset show that the performance of the proposed model is better than the existing methods.
2020, Vol. 41 
