Etsy's marketplace features diverse handmade and unique products, requiring nuanced understanding for effective search and recommendations. Current product information, while rich, is often unstructured and difficult for machine learning models to fully utilize. The core challenge lies in bridging the gap between raw data and the complex details that define each product's appeal. The solution involves using a reinforcement learning approach and contrastive signal. The method fine-tunes an LLM to generate concise product summaries emphasizing distinguishing features using buyer engagement data. This is achieved by training the model to prioritize details based on buyer choices, improving relevance predictions. The model is trained on search interaction data, rewarding summaries that highlight the features that led a buyer to choose one listing over another. This reinforcement learning drives the model to produce summaries which lead to improvements in search relevance metrics. Human evaluations and quantitative offline testing demonstrated the summaries' high quality and their impact on downstream models, improving performance. The approach focuses on understanding products based on buyer behavior, rather than rigid definitions, reflecting seller creativity. The enhanced product understanding ultimately helps buyers discover products which appeal to their tastes, thus improving the shopping experience. The resulting concise summaries highlight key characteristics that differentiate listings of similar products. The project has shown strong ability to surface the important product details compared to using only simple text features, like keywords.