قارن الطرق

	تسجيل درجة تماسك النص	التقييم الآلي للنصوص
المجال	تنقيب النصوص	تنقيب النصوص
العائلة	Process / pipeline	Process / pipeline
سنة النشأة	2008	2002 (BLEU); 2004 (ROUGE); 2020 (BERTScore)
صاحب الطريقة	Barzilay & Lapata	BLEU: Papineni et al. (2002); ROUGE: Lin (2004); BERTScore: Zhang et al. (2020)
النوع	NLP text-level scoring task	Reference-based NLG evaluation metric suite
المصدر التأسيسي	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.
الأسماء البديلة	coherence modeling, local coherence assessment, Metin Tutarlılık Puanlaması	Otomatik Metin Değerlendirme (BLEU, ROUGE, BERTScore), NLG evaluation, MT evaluation metrics
ذات صلة	4	4
الملخص	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.