This paper considers Aspect-based Opinion Summarization (AOS) of reviews on
particular products. To enable real applications, an AOS system needs to
address two core subtasks, aspect extraction and sentiment classification. Most
existing approaches to aspect extraction, which use linguistic analysis or
topic modeling, are general across different products but not precise enough or
suitable for particular products. Instead we take a less general but more
precise scheme, directly mapping each review sentence into pre-defined aspects.
To tackle aspect mapping and sentiment classification, we propose two
Convolutional Neural Network (CNN) based methods, cascaded CNN and multitask
CNN. Cascaded CNN contains two levels of convolutional networks. Multiple CNNs
at level 1 deal with aspect mapping task, and a single CNN at level 2 deals
with sentiment classification. Multitask CNN also contains multiple aspect CNNs
and a sentiment CNN, but different networks share the same word embeddings.
Experimental results indicate that both cascaded and multitask CNNs outperform
SVM-based methods by large margins. Multitask CNN generally performs better
than cascaded CNN.

该论文探讨了针对特定产品的基于方面的观点摘要 (AOS)。通过使用卷积神经网络方法，我们提出了两种方案来解决方面映射和情感分类问题，即级联的卷积神经网络和多任务卷积神经网络。实验结果表明，这两种方法均实现了 SVM 的大幅优化，多任务 CNN 相比级联 CNN 的表现更好。