In this paper, we study abstractive summarization for product reviews in the recommender systems, which aims to generate condensed text for online reviews. The summary generation is not only relevant with the content of the review itself but should be fully aware of the intrinsic features of the corresponding user and product, i.e., personalization, which are helpful to identify the saliency information in the reviews. Therefore, we propose a Rating-boosted Abstractive Review Summarization with personalized generation (RARS). In our approach, we first propose a neural review-level attention model to effectively learn user preference embedding and product characteristic embedding from their history reviews. Then, we design a personalized decoder to generate the personalized summary, which utilizes the representations of the user and the product to calculate saliency scores for words in the input review to guide the summary generation process. In addition, the rating information can explicitly indicate the sentiment opinion, hence we jointly optimize the summary generation and rating prediction through a multi-task framework, where the two tasks inherently share user preference embedding and product characteristics embedding. Extensive experiments on four datasets show that our model can effectively improve the performance of both review summarization and rating prediction.

在本文中，我们研究了推荐器系统中产品评论的抽象摘要，其目的是生成用于在线评论的压缩文本。摘要生成不仅与评论本身的内容相关，而且应充分了解相应用户和产品的固有特征（即个性化），这有助于识别评论中的显着性信息。因此，我们提出了一种带有个性化生成（RARS）的提升评级的抽象评论摘要。在我们的方法中，我们首先提出一种神经评论级别的注意力模型，以从他们的历史评论中有效地学习用户偏好嵌入和产品特征嵌入。然后，我们设计个性化的解码器以生成个性化的摘要，它利用用户和产品的表示来计算输入评论中单词的显着性得分，以指导摘要生成过程。此外，评分信息可以明确表示情感观点，因此我们通过多任务框架共同优化摘要生成和评分预测，其中两个任务本质上共享用户偏好嵌入和产品特征嵌入。在四个数据集上进行的大量实验表明，我们的模型可以有效地提高评论摘要和评分预测的性能。因此，我们通过一个多任务框架共同优化摘要生成和评分预测，这两个任务本质上共享用户偏好嵌入和产品特征嵌入。在四个数据集上进行的大量实验表明，我们的模型可以有效地提高评论摘要和评分预测的性能。因此，我们通过一个多任务框架共同优化摘要的生成和评分预测，这两个任务本质上共享用户偏好嵌入和产品特征嵌入。在四个数据集上进行的大量实验表明，我们的模型可以有效地提高评论摘要和评分预测的性能。