论证性话语的文本总结方法通过结合BERT-变压器模型
Yaser Altameemi1, Mohammed Altamimi2, Adel Alkhalil3
1Department of English, College of Arts and Literature, University of Ha'il, Ha'il, Saudi Arabia.
Frontiers in artificial intelligence
|December 15, 2025
概括
这项研究引入了一种新的提取式-抽象式文本总结方法,它结合了来自变压器的双向编码器表示 (BERT) 和转移学习,以有效地总结复杂的论证文本.
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
背景情况:
- 文本总结对于提炼主要思想至关重要.
- 提取和抽象方法在捕获完整信息方面存在局限性.
- 结合技术可以提高总结性能和质量.
研究的目的:
- 提出一种新的方法来总结复杂的文本,特别是论证性话语.
- 通过整合提取和抽象技术来提高总结性能和总结生成质量.
主要方法:
- 开发了一种混合的抽象-抽象文本总结方法.
- 利用了来自变压器的双向编码器表示 (BERT) 和转移学习.
- 将该方法应用于英国议会辩论的数据集.
主要成果:
- 提出的方法有效地总结了复杂文本的主要观点.
- 在两个辩论中获得了ROUGE-1分数30.1和36.2,ROUGE-2分数9.60和11.80,以及ROUGE-L分数27.9和31.5的分数.
- 与独立的提取或抽象方法相比,证明了更高的性能.
结论:
- 新的提取-抽象方法通过利用这两种方法的优势来增强文本总结.
- 这种混合技术确保了复杂文档中主要要点的全面覆盖.
- 该方法显示了提高自动化文本总结效率和质量的巨大潜力.
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