Сравнение методов

	Генерация естественного языка	Модель «последовательность к последовательности»
Область	Интеллектуальный анализ текста	Глубокое обучение
Семейство	Process / pipeline	Machine learning
Год появления	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	2014
Автор метода	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	Sutskever, I.; Cho, K.
Тип	NLP generative task — structured data to natural language	Encoder-decoder neural network (deep learning)
Основополагающий источник	Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170.	Sutskever, I., Vinyals, O. & Le, Q. V. (2014). Sequence to Sequence Learning with Neural Networks. NeurIPS.
Другие названия	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	Dizi-Dizi Modeli (Seq2Seq — Encoder-Decoder), encoder-decoder model, seq2seq, sequence to sequence learning
Связанные	7	5
Сводка	Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents.	The sequence-to-sequence (Seq2Seq) model, introduced by Sutskever, Vinyals and Le and by Cho and colleagues in 2014, is an encoder-decoder neural network that maps a variable-length input sequence to a variable-length output sequence. It is the foundation of machine translation, text summarization, dialogue systems and code generation.