DiCo-EXT:以多样性和一致性为导向的抽取总结框架
1College of Computer Science and Technology, Jilin University, Changchun 130012, China.
Entropy (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了DiCo-EXT,这是一种新的提取总结框架,可以减少冗余信息. 它实现了更好的摘要多样性和语义一致性,而不会牺牲ROUGE分数.
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:
- 提取性总结通常使用ROUGE分数,这可能导致冗余的句子.
- 现有的模型往往优先考虑n-gram重叠,忽视语义多样性和连贯性.
研究的目的:
- 提出 DiCo-EXT,一个用于提取总结的新型培训框架.
- 解决冗余问题,改善生成摘要中的语义多样性.
主要方法:
- 整合了两个可区分的损失术语:语义一致性和多样性处罚.
- 在标准的开采模型中优化了这些术语的基础损失.
- 避免了启发式和多阶段的后处理.
主要成果:
- 在摘要中实现了较低的冗余性和更高的词汇多样性.
- 与强大的基线相比,保持了可比的ROUGE得分.
- 在CNN/DailyMail,XSum和WikiHow数据集上证明了有效性.
结论:
- 简单的培训目标可以有效地平衡覆盖和冗余.
- 在不增加模型大小或复杂性的情况下,DiCo-EXT提高了总结质量.
- 该框架提供了一种改善提取总结的实用方法.
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