Clause-level Text Summarization Model Based on Deep Learning
The sentence-level extractive summarization model is relatively redundant and the training objectives do not match the evaluation goals in text automatic summarization task.In order to solve these problems,a clause-level text summarization model based on deep learning(CS-ASum)is proposed.Firstly,the clause-level units of the original text are extracted based on dependen-cy parsing method.Then,the vector representation of the clause are obtained by using the encoders based on BERT pre-trained model and Transformer model,and the preliminary summary candidates are received.Finally,the summary matcher is used to cal-culate the semantic similarity between the summary candidates and the original text,and the best summary is obtained.The experi-mental results on CNN/Daily Mail dataset show that,CS-ASum model is better than the compared models in both automatic evalua-tion and manual evaluation.Compared with the best-performing abstractive summarization model and extractive summarization mode,the average ROUGE value of CS-ASum model is increased by 0.76%and 1.07%respectively.Therefore,CS-ASum model acquires more concise and comprehensive summaries than the basic models in automatic text summarization task.
text summarizationdeep learningclause-leveldependency parsingROUGE evaluation
万方数据
维普
