计算机与数字工程2025，Vol.53Issue(1)：189-195,7.DOI:10.3969/j.issn.1672-9722.2025.01.035
基于文本语义的注意力指针网络文本摘要生成模型
Text Semantic-based Multi-attention Pointer Network Text Summary Generation Model
谢文博 1张晓滨1
作者信息
- 1. 西安工程大学计算机科学学院 西安 710048
- 折叠
摘要
Abstract
This paper aims to solve the problems of insufficient semantic coding and unsmooth summary sentences in the task of text summary generation,and proposes an attention pointer network text summary model based on text semantics.The model adopts the improved sequence-to-sequence(Seq2Seq)architecture,and uses double encoders and double attention mechanism to encode the source document to obtain different feature vectors of the text.The semantic feature vectors of the text are obtained by us-ing Child-Sum Tree-LSTMs+Self Attention,and the position and time sequence feature vectors of the text are obtained by using BiLSTM+SoftAttention.After that,the gating mechanism is constructed and the pointer network is fused to select the feature vectors obtained by different encoders,and the covering mechanism is used to solve the problem of generating repetition.Finally,the clus-ter search is used to select the final generated words,so as to generate a more accurate and coherent summary.Final experiment shows that,compared with the control experimental group,the model in this paper gets a higher score under the ROUGE scoring standard on the Chinese short text abstract data set LCSTS and the English data set CNN/Daily Mail,which proves that the model can effectively improve the quality of text abstracts.关键词
文本摘要生成/Child-Sum Tree-LSTMs/Seq2Seq/指针网络/注意力机制Key words
abstract generation/Child-Sum Tree-LSTMs/Seq2Seq/pointer network/attention mechanism分类
信息技术与安全科学引用本文复制引用
谢文博,张晓滨..基于文本语义的注意力指针网络文本摘要生成模型[J].计算机与数字工程,2025,53(1):189-195,7.
