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Related Questions
- Can attention mechanisms learn to generate context-specific embeddings for out-of-vocabulary words in text summarization models?
- How do attention mechanisms handle unknown words in the input text during the summarization process?
- What techniques can be applied to adapt attention-based models to handle out-of-vocabulary words in text summarization tasks?
- Can attention-based models learn to represent out-of-vocabulary words as a combination of in-vocabulary words or subwords?
- How do different attention mechanisms, such as dot-product attention and scaled dot-product attention, handle out-of-vocabulary words in text summarization?
- Can pre-training on large datasets help attention-based models to handle out-of-vocabulary words in text summarization?
- What are the implications of handling out-of-vocabulary words in attention-based models on the overall performance of text summarization systems?
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