In retrieval-augmented generation (RAG) question answering systems, generating citations for large language model (LLM) outputs enhances verifiability and helps users identify potential hallucinations. Existing citation generation methods for retrieval-augmented generation usually provide evidence at the paragraph or sentence level. Although such citations improve verifiability, they may still impose unnecessary reading burden on users: a cited sentence can contain irrelevant clauses, while an answer-bearing clause may lack sufficient contextual information such as the referred subject. In this paper, we study concise and sufficient sub-sentence citation generation, aiming to identify minimal yet self-contained evidence spans for answer verification. To this end, we first develop annotation guidelines for such citations and construct a corresponding dataset. To reduce annotation cost, we propose a data augmentation framework that generates synthetic citation annotations and filters low-quality instances using an evaluator model. Our experiments on the constructed dataset demonstrate that the proposed approach can generate high-quality and more readable citations. The code and data will be publicly released.