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Related Questions
- What techniques can be used to calibrate and validate saliency maps in text summarization models?
- How can saliency maps help identify bias or unfair treatment in text summaries?
- What impact do various summarization criteria (e.g., keywords, content, entities, sentiment) have on generated saliency maps in text summarization models?
- Can saliency maps be used in conjunction with other attribution techniques (e.g., Gradient-weighted Class Activation Maps (CAM), L1/L2 Regularization)
- What factors such as domain knowledge and document structure play a significant in the representation of salient information for text summarizations?
- Are explanations generated based on attention output, pre-trained embedding from word context, and vector representations contributing to different visualization and information in text summarized output results?
- How reliable are visual comparisons of these methods of computing salience for specific regions of importance, including parts of visualized feature attributes?
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