ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a set of metrics used to evaluate the quality of automatically generated summaries by comparing them to human-written reference summaries. Instead of assessing a summary’s correctness or coherence directly, ROUGE measures how many overlapping words, sequences of words (n-grams), and longer textual units (such as sentences) exist between the machine-generated summary and the reference. Higher ROUGE scores generally indicate a closer match to the reference summary, offering a quick, quantitative way to gauge the effectiveness of summarization algorithms.
How It Works:
- N-gram Overlap: ROUGE measures the overlap of unigrams, bigrams, trigrams, and so forth, capturing lexical similarity.
- Longest Common Subsequence (LCS): Some ROUGE variants look at the longest sequence of words that appear in both the candidate and reference texts.
- Sentence-Level Comparisons: Beyond individual words and sequences, certain ROUGE metrics consider sentence-level matches to assess structural similarity.
Why It Matters:
ROUGE provides a standardized, automated way to evaluate summarization models. While it doesn’t fully capture human notions of quality—such as clarity, coherence, or factual accuracy—it allows researchers and developers to iterate and improve their models more efficiently. As a widely adopted benchmark, ROUGE helps track progress and compare different summarization approaches.
