用强化学习生成商品描述摘要，提升电商搜索相关性。
Generating Query-Relevant Document Summaries via Reinforcement Learning
- 用强化学习根据查询生成简洁摘要，目标是优化搜索相关性。
- 在离线指标上，召回率和NDCG显著提升，线上用户参与度也改善。
- 适合需要高效、精准搜索的大型电商平台使用。
电商平台的搜索系统通常仅以商品标题作为排序模型输入，受延迟限制。但标题信息不足，难以捕捉查询意图。尽管商品描述信息更丰富，但其冗长不适合实时排序，尤其对计算成本高的交叉编码器模型而言。为此，我们提出ReLSum，一种基于强化学习的框架，旨在生成简洁且与查询相关的商品描述摘要，以优化搜索相关性。该框架利用相关性得分作为奖励信号，使摘要生成与排序目标对齐，克服了以往方法中目标不一致的问题。采用可训练的大语言模型生成摘要，并输入交叉编码器排序模型。实验表明，该方法在离线指标（如召回率、NDCG）及线上用户行为指标上均有显著提升。ReLSum为大规模电商系统的搜索相关性增强提供了一种可扩展、高效的解决方案。
原文摘要 · Abstract (English)
E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predictions, as product titles often lack sufficient detail to capture query intent. While product descriptions provide richer information, their verbosity and length make them unsuitable for real-time ranking, particularly for computationally expensive architectures like cross-encoder ranking models. To address this challenge, we propose ReLSum, a novel reinforcement learning framework designed to generate concise, query-relevant summaries of product descriptions optimized for search relevance. ReLSum leverages relevance scores as rewards to align the objectives of summarization and ranking, effectively overcoming limitations of prior methods, such as misaligned learning targets. The framework employs a trainable large language model (LLM) to produce summaries, which are then used as input for a cross-encoder ranking model. Experimental results demonstrate significant improvements in offline metrics, including recall and NDCG, as well as online user engagement metrics. ReLSum provides a scalable and efficient solution for enhancing search relevance in large-scale e-commerce systems.
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