Comparar métodos

	Resumen de texto multilingüe	Resumen de texto ajustado mediante ajuste fino
Campo	Aprendizaje profundo	Aprendizaje profundo
Familia	Machine learning	Machine learning
Año de origen	2020–2021	2019–2020
Autor original	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)
Tipo	Seq2seq / encoder-decoder fine-tuning for summarization across languages	Fine-tuned sequence-to-sequence neural model
Fuente seminal	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.	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.
Alias	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
Relacionados	4	5
Resumen	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.