Somewhat disagree with the point being made here. The fundamental assumption for humans is that when summarizing we will pay more attention to important bits, a

Somewhat disagree with the point being made here. The fundamental assumption for humans is that when summarizing we will pay more attention to important bits, and sort of only give a passing mention to others if needed. For any model, the context is the only universe where it can assign importance based on previous learning (instruction tuning examples) and the prompt. For many, shortening of the text is equivalent to summarizing (when the text is not as long as a fifty page paper*). Output depends on the instruction training dataset, and seems like unless a model is trained on longer documents, it would not produce those kind of expected outputs. In a chain of thought reasoning scenario, it probably will. With Gemini, they definitely tested out long context and tuned the outputs for it to work well as it was their value prep - shown in I/O no less. I have been working on summarizing new papers using Gemini for the same purpose. I don't ask for summary though, i ask for the story the paper is trying to tell (with different sections) and get great output. Not sharing the links here, because it would be self promotion.