Uporedite metode

	Оцена кохерентности текста	Automatsko ocenjivanje teksta
Oblast	Rudarenje teksta	Rudarenje teksta
Porodica	Process / pipeline	Process / pipeline
Godina nastanka	2008	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)
Tvorac	Barzilay & Lapata	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)
Tip	NLP text-level scoring task	Reference-based NLG evaluation metric suite
Temeljni izvor	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.
Drugi nazivi	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics
Srodne	4	4
Sažetak	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.