计算机与数字工程2024，Vol.52Issue(2)：482-486,527,6.DOI:10.3969/j.issn.1672-9722.2024.02.034
基于Transformer模型的文本自动摘要生成
Automatic Text Summary Generation Based on Transformer Model
摘要
Abstract
This paper discusses the automatic generation technology of text summarization,whose task is to generate a concise summary which can express the main meaning of text.The traditional Seq2Seq structural model has limited ability to capture and store long-term features and global features,resulting in a lack of important information in the generated abstract.Therefore,this paper proposes a new abstractive summarization model called RC-Transformer-PGN(RCTP)based on the Transformer model.The model first uses an additional encoder based on bidirectional GRU to improve the Transformer model to capture sequential context representation and improve the ability to capture local information.Secondly,it introduces Pointer Generation Network and Cover-age mechanism to alleviate the problem of Out-Of-Vocabulary words and repeated words.The experimental results on CNN/Daily Mail dataset show that our proposed model is more effective than the baseline model.关键词
生成式文本摘要/Transformer模型/指针生成网络/覆盖机制Key words
abstractive summarization/Transformer model/pointer generator network/coverage mechanism分类
建筑与水利引用本文复制引用
刘志敏,张琨,朱浩华..基于Transformer模型的文本自动摘要生成[J].计算机与数字工程,2024,52(2):482-486,527,6.
