Context compression has emerged as a vital approach to mitigate both performance degradation and resource consumption for large language models (LLMs) in long context scenarios. Current state-of-the-art methods allocate the compression budget at fixed-size block granularity, either uniformly or with adaptation across blocks. Both strategies fragment sentence-level semantic units and overlook the relevance variation within each block. In this paper, we propose the Adaptive Semantic Preservation (ASP) compressor, built on two innovations: (1) To preserve semantic units, our Semantic-Preserving Segmentation partitions context along natural sentence boundaries while maintaining the morphological atomicity of words. (2) To prioritize critical information, our task-aware Adaptive Budget Allocation dynamically assigns compression budget based on each sentence’s informational salience for the task instruction. Extensive experiments across diverse long-context scenarios show that ASP outperforms strong task-aware and task-agnostic compression baselines on both Llama-3-8B-Instruct and Qwen-2.5-7B-Instruct. In particular, ASP delivers stronger robustness compared with other baselines even at the extreme 16× high compression ratio.