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.
Abstract (translated)
摘要归纳是一项非易的任务,旨在在电子商务网站上概括产品评论的主要思想。与仅关注文档中主要事实的描述的摘要相比, review summarization 不仅要概括评论中提到的主要方面,还要反映评论作者的个人风格。尽管现有的摘要归纳方法已经包括对客户和产品的历史评论,但它们通常只是简单地拼接在一起,并且毫无目的地将这两种不同信息model成一段长序列。此外,评分信息还可以提供客户偏好的高级别抽象,但它没有被大多数方法所使用。在本文中,我们提出了一种跨类型的历史评论意识到摘要归纳模型(HHRRS),该模型通过使用Contrastive Loss的Graph reasoning模块分别建模两种不同类型的历史评论和评分信息。我们采用了一个多任务框架,一起进行评论情感分类和摘要归纳。对四个基准数据集的广泛实验证明了HHRRS在两个任务上的优越性。
URL
https://arxiv.org/abs/2301.11682
