Machine learningDeep learning / NLP / CV
迁移学习与文本摘要
迁移学习与文本摘要将预训练在广泛文本语料库（如 T5、BART 或 PEGASUS）上的大型语言模型，应用于将文档浓缩成更短、连贯摘要的任务。通过重用已学习的语言知识，并在特定领域的源文档和参考摘要对上进行微调，该方法以适度的标注数据需求实现了强大的摘要质量。
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来源
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67. link ↗
- Lewis, M., Liu, Y., Goyal, N., Ghahravi, M., Mohamed, A., Chen, D., Levy, O., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 7871–7880). ACL. DOI: 10.18653/v1/2020.acl-main.703 ↗
如何引用本页
ScholarGate. (2026, June 3). Transfer Learning with Neural Text Summarization. ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-with-text-summarization
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- [需翻译标题：BERT-based Classification...]深度学习↔ compare
- 微调文本摘要深度学习↔ compare
- 句子嵌入深度学习↔ compare
- 命名实体识别的迁移学习深度学习↔ compare