A research team is pre-training an encoder-decoder model that will later be fine-tuned for abstractive text summarization. The team must decide on the most effective input corruption strategy for this pre-training phase. They are considering two primary methods:

1. Token Masking: Randomly replacing 15% of the input tokens with a special [MASK] token and training the model to predict the original tokens.
2. Sentence Shuffling: Randomly reordering the sentences within a document and training the model to reconstruct the original sentence order.

Analyze these two options. Which strategy is likely to be more beneficial for preparing a model for abstractive summarization, and why? Justify your reasoning by connecting the skills learned during pre-training to the requirements of the final summarization task.