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Related Questions
- Can feature importance accurately capture the nuances of text summarization, where multiple features often interact to produce the final output?
- How does feature importance handle the problem of correlated features, where multiple features are highly related and difficult to distinguish?
- In text summarization, feature importance may not be able to capture the impact of long-range dependencies or contextual relationships between words, right?
- Can feature importance provide insights into the model's decision-making process when the input data is high-dimensional and sparse?
- How does feature importance account for the non-linear relationships between input features and the output summary?
- Can feature importance be used to identify the most influential words or phrases in a summary, or is it more focused on the overall feature contributions?
- In what ways can feature importance be misleading or misleadingly simple, and what are some potential consequences of relying solely on feature importance for model interpretability?
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