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Related Questions
- Can model-agnostic interpretability techniques, such as permutation feature importance, capture non-linear interactions between input text features and the generated summaries?
- How do model-agnostic interpretability techniques, like permutation feature importance, handle high-dimensional input data with many text features?
- What are the limitations of permutation feature importance in capturing non-linear relationships between input text features and the generated summaries?
- Can model-agnostic interpretability techniques be used to identify the most influential text features contributing to the generated summaries?
- How can model-agnostic interpretability techniques, such as permutation feature importance, be used to identify feature interactions in text data?
- What is the relationship between model-agnostic interpretability techniques and the concept of feature importance in the context of text summarization?
- Can model-agnostic interpretability techniques be used to evaluate the impact of different text preprocessing techniques on the generated summaries?
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