So sánh phương pháp

	Tóm tắt văn bản thích ứng miền	Phân loại dựa trên BERT
Lĩnh vực	Học sâu	Học sâu
Họ	Machine learning	Machine learning
Năm ra đời	2019–2021	2019
Người khởi xướng	Multiple contributors; domain adaptation methods consolidated via transformer-era NLP (c. 2019–2021)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
Loại	Domain adaptation of sequence-to-sequence neural summarization	Pre-trained language model with fine-tuning
Công trình gốc	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
Tên gọi khác	domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
Liên quan	6	4
Tóm tắt	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.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.