So sánh phương pháp

	Tóm tắt văn bản giám sát yếu	Học tăng cường tự giám sát
Lĩnh vực	Học sâu	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2015–2020	2018–2020
Người khởi xướng	Multiple independent research groups (NLP community, 2010s–2020s)	LeCun, Y. and community (formalized ~2018–2020)
Loại	Semi-supervised / weakly supervised NLP training paradigm	Representation learning paradigm
Công trình gốc	Amplayo, R. K., & Lapata, M. (2020). Unsupervised Opinion Summarization with Noisy Autoencoder. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1934–1945.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Tên gọi khác	weak supervision summarization, distantly supervised summarization, noisy-label summarization, pseudo-label summarization	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Liên quan	1	3
Tóm tắt	Weakly supervised text summarization trains abstractive or extractive summarization models without manually annotated reference summaries. Instead of costly human labels, it exploits weak signals — heuristic rules, distant supervision, noisy automatic labels, or self-supervised objectives — to guide sequence-to-sequence or transformer models toward producing coherent, concise summaries of input documents.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.