Research On The Automatic Generation Method Of Scientific And Technological Consulting Report Based On GPT-2 Model

In the research of science and technology,the development strategy of science and technology is a crucial issue,and the difficulty lies in how to accurately judge the future trend of science and technology.The existing judgment method is mainly based on the experience of experts,which is subjective and not comprehensive enough;In addition,the judgment results are mainly presented in the form of scientific and technological consulting report,and the report needs many experts to participate in writing,which is time-consuming and labor-consuming.In view of the above problems in the judgment of science and technology trend,this thesis studies the automatic generation method of science and technology text in the field of natural language processing,so as to provide support for experts to write scientific and technological consulting reports.There are three parts in this thesis.One is to build a value evaluation index system to scientifically evaluate the value of scientific and technological information according to the needs of the scene;Second,based on the GPT-2 model of natural language processing technology,this thesis studies the method of automatic generation of scientific and technological text,so as to generate relevant text overview according to specific scientific and technological topics;Thirdly,an evaluation index is proposed to evaluate the quality of automatically generated scientific and technological long texts.Aiming at the problem that the trend of science and technology needs multi-dimensional evaluation and judgment,this thesis puts forward a value evaluation index system by analyzing the inherent attributes of the value of science and technology patents.The index system has four first-class indexes,namely technology,economy,law and patentee,and corresponding 10 second-class indexes and 41 third-class indexes.The index system can comprehensively evaluate scientific and technological content from multiple dimensions.Aiming at the high cost of scientific and technological text writing process,this thesis proposes an automatic generation method of scientific and technological long text by analyzing the static word vector and dynamic word vector model in natural language processing.This method combines GPT-2 technology of dynamic word vector model with Chinese word segmentation table of BERT model.The experimental results show that the method can generate a section of relevant scientific and technological text according to the input.Moreover,the larger the scale of the original data and the higher the degree of association with the topic,the better the quality of the text generated by the model.This method can reduce the cost of time and manpower.Aiming at the problem that traditional indicators can not evaluate the long text of science and technology,this thesis proposes a quality evaluation index of long text of science and technology by analyzing the evaluation index based on word vector method,the evaluation index based on deep learning GAN method and BLEU index in the field of machine translation.This index evaluates the long text of science and technology from four aspects:the accuracy of words,the coherence of sentences,the rationality of the structure of the article and the relevance degree of the content of the text of science and technology.It can compare the quality of many long texts of science and technology.The experimental results verify the feasibility of the evaluation index.