方法对比

	多语言文本摘要	多语言 Transformer
领域	深度学习	深度学习
方法族	Machine learning	Machine learning
起源年份	2020–2021	2019–2020
提出者	Multiple groups; popularized via mBART (Liu et al., 2020) and mT5 (Xue et al., 2021)	Devlin et al. (mBERT); Conneau et al. (XLM-R)
类型	Seq2seq / encoder-decoder fine-tuning for summarization across languages	Pre-trained cross-lingual language model
开创性文献	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.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, pp. 4171–4186. Association for Computational Linguistics.
别名	cross-lingual summarization, multilingual abstractive summarization, multilingual extractive summarization, multilingual seq2seq summarization	multilingual LM, cross-lingual transformer, mBERT-style model, multilingual pre-trained model
相关	4	4
摘要	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.	A multilingual transformer is a pre-trained language model built on the transformer architecture and trained jointly on text from dozens to over one hundred languages. Models such as mBERT and XLM-RoBERTa learn shared cross-lingual representations, enabling zero-shot or few-shot transfer: a model fine-tuned on English data can often be applied directly to French, German, Arabic, or Chinese without language-specific labels.