What does the ROUGE metric evaluate?

The ROUGE metric evaluates the quality of text summarization by comparing a generated summary to one or more reference summaries. It primarily assesses how well the generated summary captures the essential content and meaning present in the original documents it aims to summarize. ROUGE measures aspects like the overlap of n-grams (short sequences of words) between the generated and reference summaries, recall, precision, and F1 score, making it particularly valuable in natural language processing tasks that involve summarization. The effectiveness of ROUGE lies in its focus on content similarity rather than model performance metrics or neural network efficiency, which are addressed by other methods and metrics. Thus, using ROUGE allows researchers and practitioners to determine how closely a machine-generated summary aligns with human-generated summaries, thereby indicating its quality.