Not sure what you mean by summary paper, its a pretty dense topic that assumes a fair amount of prior knowledge of the fundamentals. But maybe the Meta blog post may suffice for that?
Otherwise yes there are lots of papers on this and related topics, a few dozen in fact. But here are some notable ones, a couple of them are linked in their blog post.
Ah, got it. Yea, then I'd focus on learning how RoPE works first. That will at least help you understand how the retrieval in current long context implementations is so limited.
A colleague from a discord I spend time in threw together this video a year or so ago, might be helpful as a first watch before a deep dive: https://www.youtube.com/watch?v=IZYx2YFzVNc
Covers positional encoding as a general concept first, then goes into rotary embeddings.
Otherwise yes there are lots of papers on this and related topics, a few dozen in fact. But here are some notable ones, a couple of them are linked in their blog post.
RoFormer: Enhanced Transformer with Rotary Position Embedding - https://arxiv.org/abs/2104.09864
Scaling Laws of RoPE-based Extrapolation - https://arxiv.org/abs/2310.05209
The Impact of Positional Encoding on Length Generalization in Transformers - https://arxiv.org/abs/2305.19466
Scalable-Softmax Is Superior for Attention - https://arxiv.org/abs/2501.19399