Also known as: ROUGE L
ROUGE-L is a ROUGE metric based on the longest common subsequence shared by generated text and reference text. It captures sequence-level overlap without requiring all matching words to be contiguous.
A member of the ROUGE family focused on the length of the longest common subsequence in the reference text and generated text. The following formulas calculate recall and precision for ROUGE-L: You can then use F~1~ to roll up ROUGE-L recall and ROUGE-L precision into a single metric: Consider the following reference text and generated text. Who produced? --- Human translator ML model Therefore: - The longest common subsequence is 5 (I want to of things) - The number of words in the reference text is 9. - The number of words in the generated text is 7. Consequently: ROUGE-L ignores any newlines in the reference text and generated text, so the longest common subsequence could cross multiple sentences. When the reference text and generated text involve multiple sentences, a variation of ROUGE-L called ROUGE-Lsum is generally a better metric. ROUGE-Lsum determines the longest common subsequence for each sentence in a passage and then calculates the mean of those longest common subsequences. Consider the following reference text and generated text. Who produced? --- Human translator ML model Therefore: First sentence --- 2 (Mars dry) 6 5 Consequently: