Research Of Automatic Text Summarization Based On Selective Encoding Model

Automatic text summarization technology aims at extracting important information from text to automatically generate summary,which can effectively compress and refine the information of text.This can effectively solve the problem of information overload in the Internet age with the rapid growth of information,thus greatly improving the efficiency of people's browsing and processing of information.In this paper,we found that existing models mainly use the encoding-decoding method to generate summary,however,this method lacks the process of selecting text information,which results in a large number of redundant information unrelated to summary that interfere with the generation of summary.Therefore,the main challenge is to effectively select the important information from the original text and ignore the non-critical information.In this paper,a Topic-aware Selective Encoding Model(TSEM)is designed based on the Selective Encoding Model(SEASS)to address the above challenge.The TSEM model integrates the topic information of the text into the encoder and the selective gate network as the prior knowledge to improve the model's understanding and selection of the text information.In addition,a comparison mechanism is proposed in this paper,which can make the model fully consider the difference between the summary and the original text in the process of training,so as to further improve the model's ability to select the original text information.In order to verify the validity of the model,this paper conducts a comparison experiment on Gigaword public data sets widely used in automatic text summarization technology,and evaluates the experimental results using ROUGE evaluation method.At the same time,the thesis makes repetition rate and topic similarity analysis of the summary generated by the model,and makes a qualitative analysis of the effect of the model based on specific cases.The experimental results showed that the TSEM model incorporating the topic information and using the control mechanism could achieve better results in ROUGE scores.Compared with the original SEASS model,the TSEM model improved by 1.39%,2.12%and 1.22%in rouge-1,rouge-2 and rouge-1 scores,respectively.The generated summary could contain more key information of the original text and be more consistent with the main idea of the original text.