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Related Questions
- What are some common challenges associated with using feature importance in model-agnostic interpretability for text summarization?
- How do feature importance methods struggle to provide accurate insights into the decision-making process of text summarization models?
- What are some limitations of using feature importance in model-agnostic interpretability for text summarization, particularly in complex models with many features?
- Can feature importance methods effectively handle high-dimensional text data and identify the most influential features for text summarization?
- How do feature importance methods compare to other model-agnostic interpretability techniques, such as saliency maps or feature permutation?
- What are some potential biases or artifacts that can arise when using feature importance in model-agnostic interpretability for text summarization?
- Can feature importance methods provide actionable insights for improving the performance of text summarization models, or are they primarily useful for understanding model behavior?
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