Do LLM's get "lost in the middle" during summarization as well? [D]

The paper "Lost in the Middle: How Language Models Use Long Contexts" basically talks about how LLM's struggle with the context "in the middle" when they are given a long context, and that is tested in the paper in the usecase of RAG. I was curious if LLM's would display the same characteristic in terms of a summarization task? Do we have any insights on that?

Would it be fair to assume that LLM's would showcase the exact same characteristics …

context language language models llm lost machinelearning paper rag struggle summarization talks