方法对比

	迁移学习与文本摘要	微调文本摘要
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2019–2020	2019–2020
提出者	Raffel et al. (T5); Lewis et al. (BART)	Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)
类型	Transfer learning applied to sequence-to-sequence summarization	Fine-tuned sequence-to-sequence neural model
开创性文献	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.	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.
别名	pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning	Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning
相关	4	5
摘要	Transfer Learning with Text Summarization adapts a large language model pre-trained on broad text corpora — such as T5, BART, or PEGASUS — to the task of condensing documents into shorter, coherent summaries. By reusing learned linguistic knowledge and fine-tuning on domain-specific pairs of source documents and reference summaries, this approach achieves strong summarization quality with modest labeled data requirements.	Fine-Tuned Text Summarization adapts a large pre-trained sequence-to-sequence model — such as BART, T5, or PEGASUS — to generate concise summaries of documents by training on domain-specific (document, summary) pairs. The approach yields substantially more fluent and faithful summaries than extractive or generic approaches by leveraging knowledge encoded in billions of pre-training tokens.