ROUGE Score: Recall Overlap for Generation
ROUGE measures text generation recall by counting overlapping words and phrases against a reference. It is the default metric for summarization benchmarks. Perfect paraphrases score poorly while keyword-stuffed nonsense can score high.
ROUGE evaluates generated text by counting overlapping n-grams, word pairs, and longest common sequences with human-written references, making it a recall-oriented overlap score. It is the workhorse metric for summarization systems, machine translation, and dialogue evaluation where you need to compare model outputs against gold-standard answers at scale. The dangerous trap is treating a high ROUGE score as proof of quality, because the metric rewards verbatim copying and punishes valid paraphrases or novel correct information.
Read the original → direct-llm://rougescore
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- #llm-evaluation
- #summarization
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- #generative-ai
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