A big issue in modern LLM applications is they tend to feed long context to LLM, which results in high inference cost and latency, and may exceed the context limit. Prompt compression addresses this issue by reducing the length of input context with minimum loss of generation quality, i.e, the goal of prompt compression is to shorten the LLM input while maintaining a high generation quality. To overcome these limitations, we propose SCOPE, a training-free generative prompt compression framework based on chunk-level rewriting. Unlike the existing token removal methods, our method centers at a chunking-and-summarization mechanism. Specifically, SCOPE splits a prompt into semantically coherent chunks and rewrites the chunks to be more concise. Then the chunks are reconstructed into a meaningful prompt. Additionally, we design several optimization techniques for SCOPE, effectively preserving critical information and text coherence in compression, as well as providing finer-grained control of the compression ratio. We conduct extensive evaluation on typical LLM applications like question-answering and summarization. Results show that SCOPE consistently outperforms the evaluated selective compression baselines across most settings, with particularly strong gains at high compression ratios.

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