Which metric is used to compare AI-generated text with human reference summaries?

The BLEU (Bilingual Evaluation Understudy) and ROUGE (Recall-Oriented Understudy for Gisting Evaluation) scores are metrics specifically designed for evaluating the quality of text that has been generated by AI in comparison to human-created reference summaries.

BLEU measures how many words and phrases in the generated text overlap with the reference summaries, focusing on precision but also factors in the length of the generated summaries relative to the references to penalize excessively short outputs. This is particularly relevant for tasks like machine translation and summarization, where fluency and relevance to the original text are critical.

ROUGE, on the other hand, focuses more on recall, measuring the overlap of n-grams (sequences of n words) between the generated text and the reference summaries. It is especially useful for evaluating summaries since it helps assess how much of the reference content is captured in the AI-generated text.

Both BLEU and ROUGE are widely recognized in the natural language processing community for their effectiveness in comparing text outputs, making this choice the most appropriate for evaluating the quality of AI-generated text against human standards.