A Novel Model of Generative Automatic Text Summarization Based on BART

被引：0

作者：

Wang, Yahui ^{[1
]}

Chang, Qingxia ^{[1
]}

Meng, Xuelei ^{[2
]}

机构：

[1] Foreign Languages School, Lanzhou Jiaotong University, Gansu, Lanzhou,730070, China

[2] Traffic and Transportation School, Lanzhou Jiaotong University, Gansu, Lanzhou,730070, China

来源：

IAENG International Journal of Computer Science | 2025年 / 52卷 / 02期

关键词：

Semantics;

D O I：

暂无

中图分类号：

G2 [信息与知识传播];

学科分类号：

05 ; 0503 ;

摘要：

To obtain useful information accurately and quickly from the massive text information is the most urgent need for people nowadays. The text automatic summarization technology summarizes and condenses the given source text information and generates short texts that are concise, fluent and retain key information. This paper proposes a novel automatic summarization model - Automatic Summarization Model based on BART and Actor - Critic Algorithm (BART-ACA-AST) to realize the efficient text summarization processing. The ROUGE metric system is used to evaluate the similarity and correlation between the mechanically generated text summaries and the reference summaries to assess the quality of the listed models. BERTScore is used to appraise the semantic resemblance between the rewritten summary and the reference summary more concisely. The computing results demonstrate the excellent performance of the model. The method proposed in this article can serve as a reference for the Automatic text summarization work. © (2025), (International Association of Engineers). All rights reserved.

引用

页码：507 / 514