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.

翻译：评论摘要生成是一项重要任务，旨在概括电子商务网站中产品评论的核心思想。与只需关注文档中主要事实的文档摘要不同，评论摘要不仅需要总结评论中提及的主要方面，还应反映评论作者的个性化风格。尽管现有的评论摘要方法已融合用户与产品的历史评论，但它们通常简单地将这两类异构信息拼接并作为一个长序列无差别建模。此外，评分信息能够提供用户偏好的高层抽象，但大多数方法尚未利用这一信息。本文提出异构历史评论感知的评论摘要模型（HHRRS），该模型通过图推理模块和对比损失函数，分别对带有评分信息的两类历史评论进行建模。我们采用多任务框架，联合执行评论情感分类与摘要生成。在四个基准数据集上的大量实验表明，HHRRS在两项任务上均展现出优越性能。