Also known as: ROUGE N
ROUGE-N is a family of ROUGE metrics that measures overlap of n-grams between generated text and reference text. ROUGE-1 uses individual tokens, ROUGE-2 uses two-token sequences, and higher values of N compare longer phrase patterns.
A set of metrics within the ROUGE family that compares the shared N-grams of a certain size in the reference text and generated text. For example: - ROUGE-1 measures the number of shared tokens in the reference text and generated text. - ROUGE-2 measures the number of shared bigrams (2-grams) in the reference text and generated text. - ROUGE-3 measures the number of shared trigrams (3-grams) in the reference text and generated text. You can use the following formulas to calculate ROUGE-N recall and ROUGE-N precision for any member of the ROUGE-N family: You can then use F~1~ to roll up ROUGE-N recall and ROUGE-N precision into a single metric: Suppose you decide to use ROUGE-2 to measure the effectiveness of an ML model's translation compared to a human translator's. Who produced? Bigrams | ------| Human translator I want, want to, to understand, understand a, a wide, wide variety, variety of, of things | ML model I want, want to, to learn, learn plenty, plenty of, of things | Therefore: - The number of matching 2-grams is 3 (I want, want to, and of things). - The number of 2-grams in the reference text is 8. - The number of 2-grams in the generated text is 6. Consequently: