A research team is developing a new encoder-decoder model for natural language understanding. Their goal is to create a single pre-trained model that can be effectively fine-tuned for two distinct tasks: (1) generating concise summaries of long articles, and (2) restoring missing phrases in damaged historical texts. They are considering two different pre-training objectives based on masking input text.

• Objective A: Randomly replace 15% of the individual words in the input text with a special [MASK] token. The model must predict the original words at these masked positions.
• Objective B: Replace contiguous spans of text, totaling approximately 35% of the input, with a single [MASK] token. The model must generate the entire missing text span.

Evaluate which of these two pre-training objectives is more suitable for achieving the team's dual goals. Justify your choice by explaining how each objective would likely influence the model's capabilities for the target tasks.