Porovnat metody

	Slabě dohlížená sumarizace textu	Samoučící se učení
Obor	Hluboké učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2015–2020	2018–2020
Tvůrce	Multiple independent research groups (NLP community, 2010s–2020s)	LeCun, Y. and community (formalized ~2018–2020)
Typ	Semi-supervised / weakly supervised NLP training paradigm	Representation learning paradigm
Původní zdroj	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/
Další názvy	weak supervision summarization, distantly supervised summarization, noisy-label summarization, pseudo-label summarization	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Příbuzné	1	3
Shrnutí	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.