Compara mètodes

	Resum adaptatiu al domini	Aprenentatge per transferència amb resum de text
Camp	Aprenentatge profund	Aprenentatge profund
Família	Machine learning	Machine learning
Any d'origen	2019–2021	2019–2020
Autor original	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Raffel et al. (T5); Lewis et al. (BART)
Tipus	Domain adaptation of sequence-to-sequence neural summarization	Transfer learning applied to sequence-to-sequence summarization
Font seminal	Fabbri, A. R., KryŜiński, W., McCann, B., Xiong, C., Socher, R., & Radev, D. (2021). SummEval: Re-evaluating Summarization Evaluation. Transactions of the Association for Computational Linguistics, 9, 391–409.	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.
Àlies	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning
Relacionats	6	4
Resum	Domain-adaptive text summarization fine-tunes or adapts a pre-trained sequence-to-sequence language model on a target domain corpus so that summaries conform to domain-specific vocabulary, style, and factual constraints. It bridges the gap between general-purpose summarization models trained on news or web data and specialized domains such as biomedical literature, legal documents, scientific papers, or financial reports.	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.