hardMultiple ChoiceObjective-mapped
AIF-C01 Practice Question: A legal firm uses Amazon Bedrock to generate…
A legal firm uses Amazon Bedrock to generate contract summaries. They want to evaluate the quality of summaries against human-written reference summaries. The evaluation should capture both the overlap of n-grams and the semantic similarity. Which combination of automated metrics is MOST appropriate?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
ROUGE and BERTScore
ROUGE measures n-gram overlap (recall-oriented) for summarization, while BERTScore uses contextual embeddings to capture semantic similarity. BLEU is for translation. Combining ROUGE and BERTScore gives both lexical and semantic evaluation.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Exact match and F1 score
Why it's wrong here
Exact match is too strict for summarization; F1 over tokens does not capture semantic similarity.
- ✗
ROUGE and BLEU
Why it's wrong here
BLEU is designed for machine translation, not summarization; it does not measure semantic similarity well.
- ✓
ROUGE and BERTScore
Why this is correct
ROUGE captures n-gram overlap for summarization; BERTScore captures semantic similarity via embeddings. Together they cover both lexical and semantic quality.
- ✗
BLEU and BERTScore
Why it's wrong here
BLEU is not ideal for summarization; ROUGE is the standard for n-gram overlap in summaries.
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