统一的提取-抽象总结:采用BERT和变压器模型进行增强的文档总结的混合方法
Divya S1, Sripriya N1, J Andrew2
1Department of Information Technology, SSN College of Engineering, Kalavakkam, Tamil Nadu, India.
PeerJ. Computer science
|February 7, 2025
概括
本研究引入了一种新的混合方法,用于自动化文档总结 (ADS),结合了提取和抽象的方法. 与传统的总结技术相比,该技术显著提高了摘要的相关性和质量.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 数字文档的数量不断增加,需要高效的自动化文档总结 (ADS).
- 现有的摘要方法在生成可靠的摘要方面面临挑战,这些摘要准确地捕获了突出的信息.
- 制作高质量的摘要通常依赖于提取的特征和人类定义的参数.
研究的目的:
- 引入一个智能方法,整合提取和抽象的总结技术.
- 提高源文件及其生成的摘要之间的相关性.
- 开发一个总结模型,产生类似人类的抽象总结.
主要方法:
- 使用BERT将输入句子转换为矢量表示.
- 从句子表示形式创建相似度矩阵,用于提取式摘要生成.
- 使用具有对称目标函数的变压器模型来进行抽象的改进.
- 将提取和抽象方法集成到混合总结框架中.
主要成果:
- 拟议的混合技术在CNN/DailyMail和DUC2004数据集上表现出卓越的性能.
- 使用ROUGE指标的评估证实了综合方法的有效性.
- 该方法在输入文件和生成摘要之间实现了更高的相关性.
结论:
- 混合总结技术比传统方法提供了显著的进步.
- 整合BERT和变压器模型可以获得高质量的,类似人类的摘要.
- 这种方法解决了自动化文档总结的关键挑战.
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