基于BERT-SUMOPN模型的抽取-生成式文本自动摘要

抽取式摘要可读性、准确性较差,生成式摘要存在连贯性、逻辑性的不足,此外2种摘要方法的传统模型对文本的向量表示往往不够充分、准确。针对以上问题,该文提出了一种基于BERT-SUMOPN模型的抽取-生成式摘要方法。模型通过BERT预训练语言模型获取文本向量,然后利用抽取式结构化摘要模型抽取文本中的关键句子,最后将得到的关键句子输入到生成式指针生成网络中,通过EAC损失函数对模型进行端到端训练,结合coverage机制减少生成重复,获取摘要结果。实验结果表明,BERT-SUMOPN模型在BIGPATENT专利数据集上取得了很好的效果,ROUGE-1和ROUGE-2指标分别提升了3.3%和2.5%。

Extractive-abstractive text automatic summary based on BERT-SUMOPN model

Extractive summaries have poor readability and accuracy, while abstractive summaries also have deficiencies in coherence and logic. In addition, the traditional models of the two summary methods are often insufficient and inaccurate for the vector representation of text. In response to the above problems, this paper proposes an extractive-abstractive summary method based on BERT-SUMOPN model. The model obtains the text vector through the BERT pre-trained language model, then extracts the key sentences in the text using the extractive summary model, and finally inputs the obtained key sentences into the pointer-generation network, and carries out the model through the EAC loss function for end-to-end training, combined with the coverage mechanism to reduce duplication and obtain summary results. The experimental results show that the BERT-SUMOPN model has achieved good results on the BIGPATENT patent dataset, and the ROUGE-1 and ROUGE-2 indicators have been improved by 3.3% and 2.5% respectively.