Сравнение на методи

	Адаптивно към домейн текстово резюмиране	Трансферно обучение за обобщаване на текст
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2019–2021	2019–2020
Създател	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Raffel et al. (T5); Lewis et al. (BART)
Тип	Domain adaptation of sequence-to-sequence neural summarization	Transfer learning applied to sequence-to-sequence summarization
Основополагащ източник	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.
Други названия	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
Свързани	6	4
Резюме	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.