基于关键词与指针生成网络的摘要生成算法

Summarization Algorithm Based on Key Words and Pointer Generation Network

为解决传统生成式模型在生成摘要的过程中会忽略关键词信息为摘要提供的重要线索,导致关键词信息的丢失,生成的摘要不能很好地契合原文信息,文章提出了一种以指针生成网络为骨架融合BERT预训练模型和关键词信息的摘要生成方法.首先,结合TextRank算法与基于注意力机制的序列模型进行关键词的提取,使得生成的关键词能够包含更多的原文信息.其次,将关键词注意力加入到指针生成网络的注意力机制里,引导摘要的生成.此外,我们使用双指针拷贝机制来替代指针生成网络的拷贝机制,提高拷贝机制的覆盖率.在LCSTS数据集上的结果表明,所设计的模型能够包含更多的关键信息,提高了摘要生成的准确性和可读性.

The traditional generative model ignores the important clues provided by key words in the process of abstract generation,which leads to the loss of key word information,and the generated abstract cannot agree with the original text well.In this study,an abstract generation method is proposed,which takes the pointer-generator network as the framework and integrates BERT pretraining model and key word information.Firstly,the TextRank algorithm and the sequence model based on the attention mechanism are used to extract key words from the original text,and thus the generated key words can contain more information about the original text.Secondly,the key word attention is added to the attention mechanism of the pointer-generator network to guide the generation of an abstract.In addition,we use the double-pointer copy mechanism to replace the copy mechanism of the pointer-generator network and thus improve the coverage of the copy mechanism.The results on LCSTS data sets reveal that the designed model can contain more key information and improve the accuracy and readability of generated abstracts.