ROUGE Score Calculator
Calculate ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores for summarization
Input Texts
13 tokens
12 tokens
ROUGE Scores
ROUGE-1 (Unigrams)
54.5%54.5%
54.5%
54.5%
ROUGE-2 (Bigrams)
26.1%27.3%
25.0%
26.1%
ROUGE-L (LCS)
56.0%58.3%
53.8%
56.0%
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What is ROUGE?
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics for evaluating automatic summarization and machine translation. Unlike BLEU, ROUGE is recall-oriented, focusing on how much of the reference is captured.
ROUGE is the standard evaluation metric for summarization tasks in NLP.
ROUGE Variants
ROUGE-1
Measures unigram (single word) overlap. Good for capturing content words.
ROUGE-2
Measures bigram (word pair) overlap. Better for capturing phrase-level similarity.
ROUGE-L
Uses Longest Common Subsequence. Captures sentence-level structure and word order.
Precision vs Recall vs F1
| Metric | Formula | Measures |
|---|---|---|
| Precision | overlap / hypothesis | How much of hypothesis is relevant |
| Recall | overlap / reference | How much of reference is captured |
| F1 | 2×P×R / (P+R) | Harmonic mean of P and R |
Pro Tip: Which ROUGE to Report
For summarization papers, report ROUGE-1, ROUGE-2, and ROUGE-L F1 scores. ROUGE-2 F1 is often considered the most important for comparing systems.
Frequently Asked Questions
ROUGE vs BLEU: When to use which?
Use ROUGE for summarization (recall matters more - capturing key info). Use BLEU for translation (precision matters more - avoiding errors).
What's a good ROUGE score?
It depends on the dataset. For news summarization, ROUGE-2 F1 of 15-20% is common. Always compare to baseline systems on the same dataset.
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