AIF-C01 Question 155
Select 3You are building a natural language generation (NLG) model for summarizing large text documents. To evaluate the performance of your model, which of the following metrics are most relevant for assessing the quality of generated text compared to the reference summaries?
- A
ROUGE
- B
BLEU
- C
BERTScore
- D
Mean Squared Error (MSE)
- E
F1-Score
Show answer and explanation
Correct answers: A, B, C
Explanation
For evaluating natural language generation tasks, such as text summarization, it is essential to use metrics specifically designed to assess the quality of generated text compared to reference text. ROUGE, BLEU, and BERTScore are widely used in this domain as they measure lexical and semantic similarities effectively. Metrics like MSE and F1-Score are not applicable as they are designed for numeric predictions and classification tasks, respectively.
- A. Correct.
ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a metric commonly used for text summarization tasks. It measures the overlap between words or phrases in the generated text and the reference text, making it highly relevant for evaluating NLG models.
- B. Correct.
BLEU (Bilingual Evaluation Understudy) is a metric often used for machine translation and other NLG tasks. It evaluates how closely the generated text matches the reference text by analyzing n-gram overlaps, which makes it suitable for this scenario.
- C. Correct.
BERTScore compares the contextual embeddings of the generated and reference texts using a pre-trained BERT model. It captures semantic similarities, making it a relevant metric for assessing the quality of text generation.
- D. Incorrect.
Mean Squared Error (MSE) is a regression metric used to measure the difference between predicted and actual numeric values. It is not applicable for evaluating the quality of generated text in NLG tasks.
- E. Incorrect.
F1-Score is a metric for classification tasks, evaluating the balance between precision and recall. While useful in other contexts, it is not typically used to assess the quality of generated text in NLG models.