Personalized Review Summarization by Using Graph-Based Retrieval Augmemted Generation

Shuo Shang, Xin Cheng, Yiren Xiong, Feng Guo, Shen Gao, Xiuying Chen, Feng Wang, Yongbo Wang, Dongyan Zhao, Rui Yan

Abstract: Review summarization aims to provide a summary that covers the main aspect of the product review and reflects personal preference. Existing methods employ the historical reviews of customer and product to provide useful clues for the target summary generation. However, most of the existing methods indiscriminately model the historical reviews of customer and product. Since the historical customer reviews provide the personal information while the historical product reviews provide the commonly focused aspect of the product, these two types of heterogeneous information should be separately modeled. Moreover, the review rating of the historical reviews can be seen as a high-level abstraction of the customer preference and product which have been ignored by most of the existing methods. In this paper, we propose the Heterogeneous Historical Review aware Review Summarization (HHRRS) which separately models the two types of historical reviews with the rating information by a graph reasoning module with a contrastive loss. We employ a multi-task paradigm that conducts the review sentiment classification and summarization jointly. And we also propose a novel Graph Retrieval Augmemted Review Summarization (GRARS) to model the two types of heterogeneous information in a fine-grained manner. We conduct extensive experiments on four benchmark datasets, and demonstrate the superiority of HHRRS on both tasks.

External IDs:dblp:journals/tkde/ShangCXGGCWWZY25