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使用深度学习的低资源语言的抽象文本总结.

Nida Shafiq1, Isma Hamid1, Muhammad Asif1

  • 1Department of Computer Science, National Textile University, Faisalabad, Pakistan.

PeerJ. Computer science
|June 22, 2023
PubMed
概括

一个新的深度学习模型显著改善了乌尔都语的抽象文本总结,超过了传统的机器学习方法. 这种进步有助于处理乌尔都语.

关键词:
抽象的总结 抽象的总结伯特2伯特 伯特2伯特 伯特2伯特 伯特这是LSTM的LSTM.帕斯-贝尔特 在这就是Seq-to-Seq.乌尔都语 乌尔都语 乌尔都语

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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
  • 计算语言学 计算语言学

背景情况:

  • 自动文本总结对于管理信息过载至关重要.
  • 提取和抽象方法是文本总结的主要方法.
  • 对于像乌尔都语这样的低资源语言来说,抽象的总结仍然具有挑战性.

研究的目的:

  • 开发和评估一个深度学习模型,用于乌尔都语的抽象文本总结.
  • 将拟议模型的性能与已建立的机器学习技术进行比较.

主要方法:

  • 开发了一个使用编码器-解码器范式的深度学习模型.
  • 该模型在乌尔都语100万新闻数据集上进行了训练和测试.
  • 性能与支持矢量机 (SVM) 和物流回归 (LR) 模型进行了比较.

主要成果:

  • 拟议的深度学习模型与SVM和LR相比表现优越.
  • 系统生成的摘要被乌尔都语语言专家验证.
  • 该模型显示,抽象总结的准确性显著提高.

结论:

  • 深度学习为乌尔都语的抽象文本总结提供了一个有希望的方法.
  • 开发的模型有效地解决了摘要乌尔都文本的挑战.
  • 需要对乌尔都语的抽象总结进行进一步的研究.