Сравнение на методи

	Фино настроено обобщаване на текст	Фино настроено отговаряне на въпроси
Област	Дълбоко обучение	Дълбоко обучение
Семейство	Machine learning	Machine learning
Година на възникване	2019–2020	2016–2019
Създател	Lewis et al. (BART); Zhang et al. (PEGASUS); Raffel et al. (T5)	Devlin et al. (BERT); Rajpurkar et al. (SQuAD benchmark)
Тип	Fine-tuned sequence-to-sequence neural model	Transfer learning / fine-tuning for extractive or generative QA
Основополагащ източник	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.	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, 4171–4186.
Други названия	Fine-tuned summarization model, Abstractive summarization via fine-tuning, Seq2Seq fine-tuning for summarization, BART/T5/PEGASUS fine-tuning	fine-tuned QA, neural QA with fine-tuning, extractive QA fine-tuning, reading comprehension fine-tuning
Свързани	5	5
Резюме	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.	Fine-Tuned Question Answering adapts a large pre-trained language model — such as BERT, RoBERTa, or a GPT-family model — to answer natural-language questions over a given context passage or knowledge base. The model learns to locate answer spans or generate free-form answers by continuing training on labeled QA pairs after general-purpose pre-training.