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Related Questions
- What are common sources of bias in text summarization models?
- How can data curation techniques, such as filtering and debiasing, reduce bias in text summarization models?
- What role do word embeddings play in perpetuating bias in text summarization models?
- How can augmenting training datasets with balanced and diverse samples help to mitigate bias in text summarization models?
- What algorithmic modifications, such as fairness-based optimization and ensemble methods, can be employed to address bias in text summarization models?
- Can explainability techniques, such as saliency maps and feature importance, help to identify and quantify bias in text summarization models?
- How can human evaluation and review processes be integrated into the development and deployment of text summarization models to ensure fairness and accuracy?
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