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半监督文本摘要

半监督文本摘要通过利用大量无标签文本以及少量人工编写的参考摘要来训练摘要模型。通过使用语言模型预训练、伪标签和自训练等技术,这些方法在保持基准数据集上具有竞争力的ROUGE分数的同时,大大减轻了标注负担。

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来源

  1. He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., & Neubig, G. (2020). Revisiting Semi-Supervised Learning for Neural Sequence Generation. In Proceedings of ICLR 2020. link
  2. Automatic summarization. Wikipedia. link

如何引用本页

ScholarGate. (2026, June 3). Semi-supervised Text Summarization (Label-efficient Abstractive and Extractive Summarization). ScholarGate. https://scholargate.app/zh/deep-learning/semi-supervised-text-summarization

ScholarGateSemi-supervised Text Summarization (Semi-supervised Text Summarization (Label-efficient Abstractive and Extractive Summarization)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/semi-supervised-text-summarization · 数据集: https://doi.org/10.5281/zenodo.20539026