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| Адаптивно към домейн текстово резюмиране× | Фино настроено 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) | Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5) |
| Тип≠ | Domain adaptation of sequence-to-sequence neural summarization | Fine-tuned sequence-to-sequence neural model |
| 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 ↗ | Zhang, J., Zhao, Y., Saleh, M., & Liu, P. J. (2020). PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization. Proceedings of the 37th International Conference on Machine Learning (ICML), 119, 11328–11339. link ↗ |
| Други названия | domain-adapted summarization, domain-specific summarization, cross-domain summarization, DA-summarization | Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning |
| Свързани≠ | 6 | 5 |
| 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. | Fine-Tuned Text Summarization adapts a large pre-trained sequence-to-sequence model — such as BART, T5, or PEGASUS — to generate concise summaries of documents by training on domain-specific (document, summary) pairs. The approach yields substantially more fluent and faithful summaries than extractive or generic approaches by leveraging knowledge encoded in billions of pre-training tokens. |
| ScholarGateНабор от данни ↗ |
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