Research And Implementation Of Text Summarization Technology Based On Semantic Understanding

With the society entering the information age,the huge amount of information on the Internet makes it very difficult for users to quickly retrieve effective information.The problem above is solved by the emerging automatic text summarization technology,but the traditional automatic text summarization technology is limited by various conditions,which makes it difficult to generate a good summarization with high accuracy,fluent and concise statements.On the basis of the above problems,this thesis mainly investigates how to improve the quality of Chinese automatic text summarization.The specific research contents are as follows:1.We propose a generative text summarization model based on semantic understanding.The traditional Chinese text summarization model is difficult to take advantage of the correlation between the original text entities,resulting in low accuracy of the generated summaries.In response to this problem,we uses the original text to construct a textual semantic understanding graph,which enhances the relevance of the text entity and its related content.At the same time,in order to integrate the text semantic understanding graph into the model reasonably,we improve the text semantic understanding graph encoder based on the graph attention mechanism.Finally,an decoder that combines text and graph information is designed,so that the model combines text encoding information and constructed semantic understanding graph information when generating summarization,thereby enhancing the accuracy of the model generated summaries.We conducted comparative experiments on the LCSTS dataset to verify the effectiveness of the model.2.We improve the generative text summarization model based on semantic understanding.To solve the problem that the model generated summary is not concise enough,we make improvements from two aspects: one is to improve the contrastive attention mechanism,so that the model can focus on the important information in the original text,and the other is using BERT as the embedding layer of the model to make it effective to extract the features in the original text.Finally,a comparative experiment confirms that the above improvements have prefected the conciseness of generating summaries.3.Based on the above work,we design and implement an automatic text summarization system for the Chinese news field.The system takes the algorithm model proposed in this thesis as the core,based on the B/S architecture,and realizes the frontend display layer,the communication layer and the automatic summary business layer,and the system has achieved the functions and interface.After testing the system,this thesis shows that the system can generate high-quality news summaries to meet actual application requirements.