Also known as: ROUGE (Recall-Oriented Understudy For Gisting Evaluation) · ROUGE · Recall-Oriented Understudy For Gisting Evaluation
Recall-Oriented Understudy for Gisting Evaluation (ROUGE) es una familia de métricas para evaluar resúmenes y salidas de traducción automática midiendo el solapamiento con texto de referencia. Sus distintas variantes comparan unidades como tokens, n-gramas, skip-grams o subsecuencias comunes más largas.
A family of metrics that evaluate automatic summarization and machine translation models. ROUGE metrics determine the degree to which a reference text overlaps an ML model's generated text. Each member of the ROUGE family measures overlap in a different way. Higher ROUGE scores indicate more similarity between the reference text and generated text than lower ROUGE scores. Each ROUGE family member typically generates the following metrics: - Precision - Recall - F~1~ [!NOTE] Note: ROUGE uses precision and recall somewhat differently than traditional precision and recall. For details and examples, see: - ROUGE-L - ROUGE-N - ROUGE-S [!NOTE] Note: BLEU and BLEURT optimize for precision while ROUGE optimizes for recall. Consequently, BLEU and BLEURT are better metrics for evaluating machine translation (since the focus is precision) while ROUGE is a better metric for summarization (since the focus is recall).