Large language models (LLMs) trained on massive text corpora may inadvertently memorize sensitive or copyrighted content, motivating the need for more targeted unlearning. Selective LLM unlearning focuses on identifying token-level or span-level unlearning targets within a text, rather than treating entire sequences as unlearning targets. However, many existing selective approaches depend on external supervision to identify unlearning targets, which may misalign unlearning objectives with the model’s internal behavior. In this paper, we propose a selective span-level unlearning method that is grounded entirely in model-intrinsic information. Our method first estimates token-level importance scores by contrasting gradient information induced by forget and retain datasets, identifying tokens that disproportionately contribute to information targeted for unlearning. These token-level importance scores are then used as anchors to identify coherent span-level unlearning targets via a self-consistency–based generation process, allowing the model to determine stable spans based on its own predictions. Experiments on two LLM unlearning benchmarks show that our approach achieves comparable unlearning performance while substantially better preserving retained knowledge.