Design And Implementation Of Automatic Keyword Generation System

In the academic field,it is often necessary to analyze the paper by keywords.The keywords provide highly general paper information,which is the basis of information retrieval,automatic classification,automatic clustering and viewpoint mining.With the rapid increase of network information,manual identification of keywords is time-consuming and subjective,so the automatic generation of keywords become a research hotpot.The traditional automatic keyword generation algorithm mainly uses statistical data to generate keywords without considering the semantic information in the text.With the rapid development of deep learning and natural language processing,the current keyword generation algorithm can use Sequence-to-Sequence framework and attention mechanism to understand the text,so as to generate keywords automatically.In recent years,due to the excellent performance of Transformer in the field of natural language processing and the rapid development of attention-related mechanisms,the automatic keyword generation algorithm has room for further improvement in accuracy and diversity.This research combines the Sequence-to-Sequence framework,copy mechanism and coverage mechanism based on Transformer,and designs the automatic keyword generation system.This research is divided into three steps: Firstly,the set of keywords is preprocessed into different sequences of keywords and trained as the input of decoder.Secondly,the system modifies the structure of the original Transformer,and applies copy mechanism and coverage mechanism to alleviate the problem that keywords are not in the prediction vocabulary and the problem of keyword generation diversity.Finally,the system searches the target keywords by beam search,balancing the generation time and quality of keywords.The main contributions of this research are as follows: Firstly,the system can understand the text semantics and predict the keywords that do not appear in the original text.Secondly,the system uses five data sets commonly used in keyword automatic generation tasks,and exceeds the Copy RNN model in the ability of keyword automatic generation.Finally,the system studies the keyword generation model based on Transformer from different aspects such as order of keywords and framework,and explains and analyzes the experimental results.