ROUGE (metric) explained

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 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)

External links

• ROUGE Usage Tutorial
• Java Implementation of ROUGE

Notes and References

1. http://www.aclweb.org/anthology/W/W04/W04-1013.pdf 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.
2. http://aclweb.org/anthology/N/N03/N03-1020.pdf 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. http://aclweb.org/anthology/P/P04/P04-1077.pdf 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.