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.

本文提出了Heterogeneous Historical Review aware Review Summarization Model (HHRRS)，它结合了历史客户和产品的评分信息，采用图推理模块以对比损失函数分别对这两种历史评论进行建模。同时本文使用多任务框架，对评论情感分类和摘要进行联合训练，并在四个基准数据集上进行了广泛的实验证明了HHRRS的优越性。

分别对顾客和产品的历史评价进行建模，实现个性化评论摘要