A machine learning engineer is tasked with fine-tuning a language model for two different applications: (1) summarizing legal documents, where key information is often found in specific, predictable sections, and (2) analyzing real-time social media feeds, where important context can appear anywhere in a long stream of posts.

The engineer is considering two methods to make the model's attention mechanism more efficient: one that approximates the full attention matrix by assuming it has a low-rank structure, and another that restricts each token to only attend to a predefined, limited set of other tokens.

Analyze the fundamental assumptions behind these two efficiency-improving approaches. Based on your analysis, evaluate which approach is likely to be more effective for each of the two applications and justify your reasoning.