Порівняння методів

	Оцінювання когерентності тексту	Автоматична оцінка тексту
Галузь	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту
Родина	Process / pipeline	Process / pipeline
Рік появи	2008	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)
Автор методу	Barzilay & Lapata	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)
Тип	NLP text-level scoring task	Reference-based NLG evaluation metric suite
Основоположне джерело	Barzilay, R. & Lapata, M. (2008). Modeling Local Coherence: An Entity-Based Approach. Computational Linguistics, 34(1), 1-34.	Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A Method for Automatic Evaluation of Machine Translation. Proceedings of ACL 2002.
Інші назви	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics
Пов'язані	4	4
Підсумок	Text coherence scoring computes a document-level coherence score with machine learning, rooted in the entity-based local coherence model introduced by Barzilay and Lapata (2008). It measures how well the sentences of a text hang together, using either an entity-grid model, a graph-based approach, or a transformer-based model.	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.