E-Commerce (EC) websites provide a large amount of useful information that
exceed human cognitive processing ability. In order to help customers in
comparing alternatives when buying a product, previous studies designed opinion
summarization systems based on customer reviews. They ignored templates'
information provided by manufacturers, although these descriptive information
have much product aspects or characteristics. Therefore, this paper proposes a
methodology coined as SEOpinion (Summa-rization and Exploration of Opinions)
which provides a summary for the product aspects and spots opinion(s) regarding
them, using a combination of templates' information with the customer reviews
in two main phases. First, the Hierarchical Aspect Extraction (HAE) phase
creates a hierarchy of product aspects from the template. Subsequently, the
Hierarchical Aspect-based Opinion Summarization (HAOS) phase enriches this
hierarchy with customers' opinions; to be shown to other potential buyers. To
test the feasibility of using Deep Learning-based BERT techniques with our
approach, we have created a corpus by gathering information from the top five
EC websites for laptops. The experimental results show that Recurrent Neural
Network (RNN) achieves better results (77.4% and 82.6% in terms of F1-measure
for the first and second phase) than the Convolutional Neural Network (CNN) and
the Support Vector Machine (SVM) technique.

该研究提出了一种名为 SEOpinion 的方法，使用模板信息和客户评论结合的方式，为产品方面提供总结并发现对其的意见，通过层次化的产品方面提取和基于产品方面的意见总结两个主要阶段，利用深度学习的 BERT 技术进行测试。实验结果表明，循环神经网络（RNN）在第一和第二阶段的 F1 值方面取得了比卷积神经网络（CNN）和支持向量机（SVM）技术更好的结果（分别为 77.4% 和 82.6%）。