A team runs an automated Bedrock model evaluation job to compare two foundation models for a news-article summarization feature. Their evaluation dataset uses reference summaries written by editors, but the two models express the same key facts using different wording and sentence structures. The team notices that a model producing summaries they consider high-quality receives surprisingly low scores on the current metric, because it rarely reuses the exact tokens or n-grams from the reference text. Which change to their evaluation approach best addresses this problem while remaining automated?