Progressive summarization works pretty well - I'm using that for https://findsight.ai
You can even get lesser LLMs to do the bulk reduction that have GPT clean it up on the way to even less content. Admittedly, that does take a lot of prompt engineering, chunk selection and reinforcement though (LLM supervising LLM).
I don't think this approach is formalized but I can give a few examples:
A) Prompt leak prevention: chunk and embed LLM responses, than compare against original prompt to filter out chunks that leak the prompt
B) Automatic prompt refinement: Prompt a cheap model, use an expensive model to judge the output and rewrite the prompt (this is in part how Vicuna[1] did eval for their LLaMa fine-tuning)
You can even get lesser LLMs to do the bulk reduction that have GPT clean it up on the way to even less content. Admittedly, that does take a lot of prompt engineering, chunk selection and reinforcement though (LLM supervising LLM).