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| Tóm tắt văn bản đa ngôn ngữ× | Tóm tắt văn bản tinh chỉnh× | |
|---|---|---|
| Lĩnh vực | Học sâu | Học sâu |
| Họ | Machine learning | Machine learning |
| Năm ra đời≠ | 2020–2021 | 2019–2020 |
| Người khởi xướng≠ | Multiple groups; popularized via mBART (Liu et al., 2020) and mT5 (Xue et al., 2021) | Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5) |
| Loại≠ | Seq2seq / encoder-decoder fine-tuning for summarization across languages | Fine-tuned sequence-to-sequence neural model |
| Công trình gốc≠ | Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021). mT5: A Massively Multilingual Pre-Trained Text-to-Text Transformer. Proceedings of NAACL-HLT 2021, pp. 483–498. Association for Computational Linguistics. 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 ↗ |
| Tên gọi khác | cross-lingual summarization, multilingual abstractive summarization, multilingual extractive summarization, multilingual seq2seq summarization | Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning |
| Liên quan≠ | 4 | 5 |
| Tóm tắt≠ | Multilingual text summarization applies pre-trained multilingual encoder-decoder models — such as mT5 or mBART — to generate concise summaries of documents written in many languages, either within the same language (monolingual) or across languages (cross-lingual). Fine-tuning these models on multilingual summarization benchmarks like XL-Sum enables coverage of dozens of languages with a single model. | 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. |
| ScholarGateBộ dữ liệu ↗ |
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