ROUGE (metric)
From HandWiki
Short description: Metric used for testing NLP models
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation,[1] is a set of metrics and a software package used for evaluating automatic summarization and machine translation software in natural language processing. The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation. ROUGE metrics range between 0 and 1, with higher scores indicating higher similarity between the automatically produced summary and the reference.
Metrics
The following five evaluation metrics are available.
- ROUGE-N: Overlap of n-grams[2] between the system and reference summaries.
- ROUGE-1 refers to the overlap of unigrams (each word) between the system and reference summaries.
- ROUGE-2 refers to the overlap of bigrams between the system and reference summaries.
- ROUGE-L: Longest Common Subsequence (LCS)[3] based statistics. Longest common subsequence problem takes into account sentence-level structure similarity naturally and identifies longest co-occurring in sequence n-grams automatically.
- ROUGE-W: Weighted LCS-based statistics that favors consecutive LCSes.
- ROUGE-S: Skip-bigram[3] based co-occurrence statistics. Skip-bigram is any pair of words in their sentence order.
- ROUGE-SU: Skip-bigram plus unigram-based co-occurrence statistics.
See also
- BLEU
- F-Measure
- METEOR
- NIST (metric)
- Noun-phrase chunking
- Word error rate (WER)
References
- ↑ Lin, Chin-Yew. 2004. ROUGE: a Package for Automatic Evaluation of Summaries. In Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 25 - 26, 2004.
- ↑ Lin, Chin-Yew and E.H. Hovy 2003. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics. In Proceedings of 2003 Language Technology Conference (HLT-NAACL 2003), Edmonton, Canada, May 27 - June 1, 2003.
- ↑ 3.0 3.1 Lin, Chin-Yew and Franz Josef Och. 2004. Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona, Spain, July 21 - 26, 2004.
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