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	Słabo nadzorowane streszczanie tekstu	Uczenie samo nadzorowane
Dziedzina	Uczenie głębokie	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2015–2020	2018–2020
Twórca	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
Źródło pierwotne	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/
Inne nazwy	weak supervision summarization, distantly supervised summarization, noisy-label summarization, pseudo-label summarization	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Pokrewne	1	3
Podsumowanie	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.