Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately

• URL: http://arxiv.org/abs/2301.11682v1
• Date: Fri, 27 Jan 2023 12:32:55 GMT
• Title: Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately
• Authors: Xin Cheng, Shen Gao, Yuchi Zhang, Yongliang Wang, Xiuying Chen, Mingzhe Li, Dongyan Zhao and Rui Yan
• Abstract summary: Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website. We propose the Heterogeneous Historical Review aware Review Summarization Model (HHRRS) We employ a multi-task framework that conducts the review sentiment classification and summarization jointly.
• Score: 59.61932899841944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website. Different from the document summary which only needs to focus on the main facts described in the document, review summarization should not only summarize the main aspects mentioned in the review but also reflect the personal style of the review author. Although existing review summarization methods have incorporated the historical reviews of both customer and product, they usually simply concatenate and indiscriminately model this two heterogeneous information into a long sequence. Moreover, the rating information can also provide a high-level abstraction of customer preference, it has not been used by the majority of methods. In this paper, we propose the Heterogeneous Historical Review aware Review Summarization Model (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 framework that conducts the review sentiment classification and summarization jointly. Extensive experiments on four benchmark datasets demonstrate the superiority of HHRRS on both tasks.

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