In exploratory search, users often submit vague queries to investigate unfamiliar topics, but receive limited feedback about how the search engine understood their input.This leads to a self-reinforcing cycle of mismatched results and trial-and-error reformulation.To address this, we study the task of generating user-facing natural language query intent descriptions that surface what the system likely inferred the query to mean, based on post-retrieval evidence.We propose QUIDS, a method that leverages dual-space contrastive learning to isolate intentrelevant information while suppressing irrelevant content.QUIDS combines a dual-encoder representation space with a disentangling decoder that works together to produce concise and accurate intent descriptions.Enhanced by intent-driven hard negative sampling, the model significantly outperforms state-of-theart baselines across ROUGE, BERTScore, and human/LLM evaluations.Our qualitative analysis confirms QUIDS' effectiveness in generating accurate intent descriptions for exploratory search.Our work contributes to improving the interaction between users and search engines by providing feedback to the user in exploratory search settings.1

Computer Systems, Imagery and Media