Bandingkan kaedah

	Pembelajaran Pemindahan dengan Peringkasan Teks	Ringkasan Teks yang Ditala Halus
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2019–2020	2019–2020
Pengasas	Raffel et al. (T5); Lewis et al. (BART)	Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)
Jenis	Transfer learning applied to sequence-to-sequence summarization	Fine-tuned sequence-to-sequence neural model
Sumber perintis	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.
Alias	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
Berkaitan	4	5
Ringkasan	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.