Abstract: Query reduction aims to enhance retrieval recall and precision by simplifying and condensing lengthy queries while retaining key information. Traditional methods often rely on statistical approaches or pre-trained models to extract keywords from lengthy queries for retrieval input. However, these methods struggle with query complexity(e.g., synonym and polyseme)and often lose crucial information. To address these issues, a method integrating key concepts and latent concepts for verbose query reduction is proposed. This approach integrates key concepts representing the core content of the query with latent concepts crucial for query understanding but not explicitly expressed to generate more comprehensive and effective queries. Specifically, pre-trained models generate concise and effective queries as key concepts, while pseudo-relevance feedback methods extract latent concepts from relevant document sets of the original query. Finally, both are combined to form the query reduction for improved retrieval. Experimental results on the Robust2004 dataset using a dense retrieval model show that the proposed method improves R@1000 and NDCG@10 by 2.1% and 3.6%, respectively, compared to baseline models.

Key words: information retrieval, verbose query, query reduction, key concept, latent concept