Text Summarization Research Based On Semantic Analysis

Automatic text summarization is an important application direction in the field of natural language processing,aiming to automatically generate a concise and short text containing the main information and key points of the input text.With the promotion and development of deep learning,it has become possible to use computers to automatically understand text and generate summaries.Text summarization has made great breakthroughs in recent years,but there are still many challenges.For example,how to accurately locate key information in the text while considering the semantic relevance between information is a challenge.At the same time,how the model can learn more comprehensive semantic information is also a major difficulty.Therefore,based on the model’s ability to analyze the semantic information of text,this paper improves and optimizes existing text summarization techniques.The main research contents are summarized as follows:(1)In order to simultaneously extract global semantic features and enhance the local correlations between the contextual information,this paper proposes a generative text summarization method that enhances local relevance.This method filters the context vectors using a gating unit,and applies the idea of residual networks in the gating unit to incorporate the semantic features of each word when extracting text n-gram features.Additionally,an adaptive algorithm is introduced to normalize the word embeddings and text features.Experimental results show that the summaries generated by this method have higher recall rates and better readability compared to other advanced models.(2)To enhance the text semantic extraction ability of the model,this paper proposes a BERT+Seq2seq summarization model based on a gated attention mechanism.First,BERT text representation is introduced as the input of the model to make the word vectors in the input model more semantically rich.Second,an efficient gated attention mechanism is adopted,which combines the advantages of attention and gating mechanisms,to better handle semantic extraction and information filtering in text summarization tasks,while improving the performance of the model.Experimental results on the LCSTS dataset show that this method has stronger semantic information extraction ability and significantly improves the quality of generated summaries.(3)A community security intelligence analysis application based on text summarization has been implemented,which can receive real-time community intelligence,extract key information,and judge the existence of dangerous information based on the summary and other information,and output corresponding warnings.Practical tests have shown that the system has excellent intelligence analysis performance and is of great significance for community security prevention and control.In summary,this paper designs two text summarization models.Experimental results show that the models have stronger semantic extraction abilities.In addition,applying text summarization techniques to community intelligence security analysis has achieved certain effects,which is of great significance for future applications of natural language processing technology in community security prevention and control tasks.