手法を比較

	自然言語生成	自動テキスト評価
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era)	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)
提唱者	Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018)	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)
種類	NLP generative task — structured data to natural language	Reference-based NLG evaluation metric suite
原典	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.	Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.
別名	NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG)	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics
関連	7	4
概要	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.	Automatic text evaluation is a family of reference-based metrics used to measure the quality of machine-generated text — such as translations, summaries, or natural-language-generation (NLG) outputs — by comparing them to one or more human-written reference texts. Pioneered by Papineni et al. with BLEU in 2002, the field has grown to include n-gram overlap metrics (BLEU, ROUGE) and semantically aware metrics (BERTScore, MoverScore) that capture meaning beyond surface word matches.