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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。
速览
Year 2018–2020
Type Semi-supervised sequence-to-sequence learning
DataType Unlabeled and labeled text corpora
Subfamily Deep learning / NLP / CV
来源 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 ↗ Automatic summarization. Wikipedia. link ↗ 如何引用本页 APA BibTeX RIS 复制
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
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ScholarGate — Semi-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