Сравнение на методи
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| Трансферно обучение за обобщаване на текст× | Фино настроено обобщаване на текст× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване | 2019–2020 | 2019–2020 |
| Създател≠ | Raffel et al. (T5); Lewis et al. (BART) | Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5) |
| Тип≠ | Transfer learning applied to sequence-to-sequence summarization | Fine-tuned sequence-to-sequence neural model |
| Основополагащ източник≠ | 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. link ↗ | 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 | pretrained summarization model, fine-tuned summarization, TL-summarization, neural abstractive summarization via transfer learning | Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning |
| Свързани≠ | 4 | 5 |
| Резюме≠ | 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. | 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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