Text summarization helps users manage information overload, but traditional methods can be cumbersome when seeking specific details within a document. Aspect-based text summarization addresses this by using a query to guide which information should be summarized. However, distinguishing relevant from irrelevant information for a given aspect remains challenging in LLM-based summarization models. In this work, we propose utilizing contrastive learning to encourage LLMs to focus on aspect-related signals during training. We further design two variants of the learning algorithm, aspect-anchored and summary-anchored, corresponding to the strategies used in constructing negative examples. Evaluation with two representative LLM families (Llama 2 and Pythia) and two benchmark datasets (AnyAspect and CovidET) demonstrates the proposed methods’ strong performance compared to their supervised fine-tuning and zero-shot counterparts, highlighting contrastive learning as a promising direction for aspect-based text summarization.