Machine learningDeep learning / NLP / CV
微调文本摘要
微调文本摘要通过在特定领域（文档、摘要）对上进行训练，使大型预训练序列到序列模型（如 BART、T5 或 PEGASUS）能够生成简洁的文档摘要。该方法通过利用预训练中编码的知识，可以生成比抽取式或通用方法更流畅、更忠实的摘要。
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来源
- Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2020). PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 11328–11339. link ↗
- Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 7871–7880. DOI: 10.18653/v1/2020.acl-main.703 ↗
如何引用本页
ScholarGate. (2026, June 3). Fine-Tuned Pre-trained Sequence-to-Sequence Model for Text Summarization. ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-text-summarization
Which method?
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