A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities.
Which metric should the company use to evaluate the accuracy of the model?
BERTScore: This metric leverages the capabilities of a pre-trained BERT model to assess the semantic similarity between the generated summaries and the reference text, providing a more accurate evaluation of the model's ability to capture the key points of the original text, which is crucial for text summarization.
BERTScore is the most appropriate metric for evaluating the accuracy of a generative text summarization model because it compares semantic similarity in a manner that aligns well with the goal of text summarization.
BERTScore is a metric specifically designed to evaluate text generation tasks, such as summarization. It measures the semantic similarity between the generated text and the reference text by leveraging contextual embeddings from pre-trained models like BERT.
BERTScore captures deeper semantic relationships, making it ideal for evaluating the accuracy and meaningfulness of summaries.
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