Learning to Summarize for Search Relevance with Reinforcement Learning

Abstract

E-commerce search ranking models face the challenging and critical problem of balancing strict real-time latency constraints with the need for high-quality relevance predictions. In production environments, ranking models often rely primarily on product titles, which frequently omit critical attributes required to satisfy diverse query intents. While full product descriptions provide richer information, their length and verbosity make them computationally impractical for real-time ranking, particularly when using cross-encoder architectures. To address this challenge, we propose ReLSum, a reinforcement learning framework that generates concise, relevance-optimized product summaries for search ranking. ReLSum directly aligns summarization with the ranking objective by using downstream relevance scores as reward signals. The framework conditions the Large Language Model (LLM) solely on product information, while queries are used only to compute rewards during training. This design enables summaries to be generated and cached offline, ensuring no additional inference latency at serving time. Experiments on large-scale production data show substantial improvements in offline NDCG and recall. In online A/B tests, ReLSum delivers statistically significant gains in user engagement metrics such as orders per visitor and units per completed order, with particularly strong improvements for tail queries.