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Related Questions
- How does the Local Interpretable Model-agnostic Explanations (LIME) method handle categorical features in text summarization models?
- Can LIME be used to identify feature importance in neural network-based text summarization models?
- What are some common pitfalls to avoid when using LIME for feature importance analysis in text summarization models?
- How does the choice of sampling method in LIME affect the accuracy of feature importance scores in text summarization models?
- Can LIME be used to compare the interpretability of different text summarization models, such as extractive and abstractive models?
- How does the dimensionality of the feature space impact the performance of LIME in text summarization models?
- Can LIME be used to identify feature importance in text summarization models that use word embeddings, such as BERT or transformers?
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