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| Адаптивно към домейн текстово резюмиране× | Класификация, базирана на BERT× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2019–2021 | 2019 |
| Създател≠ | 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) |
| Тип≠ | Domain adaptation of sequence-to-sequence neural summarization | Pre-trained language model with fine-tuning |
| 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. DOI ↗ | 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. DOI ↗ |
| Други названия | domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization | BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS |
| 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. | 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. |
| ScholarGateНабор от данни ↗ |
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