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Related Questions
- Can feature importance measures be misleading in text summarization due to the complex relationships between words and their importance in the context?
- How can the use of feature importance techniques in text summarization lead to over-reliance on individual words rather than the overall context?
- In what ways can model interpretability techniques be used to create a false sense of understanding in text summarization, masking underlying biases or errors?
- Can the focus on feature importance in text summarization distract from the need to consider the broader semantic relationships between words and concepts?
- How can the misuse of feature importance techniques in text summarization impact the accuracy and reliability of the summarization results?
- In what ways can model interpretability techniques be used to identify and exploit biases in the training data, leading to unfair or inaccurate summarization results?
- Can the use of feature importance techniques in text summarization lead to a lack of transparency and accountability in the decision-making process?
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