Current opinion summarization systems simply generate summaries reflecting
important opinions from customer reviews, but the generated summaries may not
attract the reader's attention. Although it is helpful to automatically
generate professional reviewer-like summaries from customer reviews, collecting
many training pairs of customer and professional reviews is generally tricky.
We propose a weakly supervised opinion summarization framework, Noisy Pairing
and Partial Supervision (NAPA) that can build a stylized opinion summarization
system with no customer-professional review pairs. Experimental results show
consistent improvements in automatic evaluation metrics, and qualitative
analysis shows that our weakly supervised opinion summarization system can
generate summaries that look more like those written by professional reviewers.

本文提出了一种弱监督学习的意见摘要框架 NAPA，不需要客户和专业评论对作为训练数据，在自动评估指标和定性分析上得到了一致的改进，表明我们的系统可以生成类似专业评论员撰写的摘要。

用嘈杂对和局部监督进行观点摘要

Noisy Pairing and Partial Supervision for Opinion Summarization

Sentiment analysis provides a useful overview of customer review contents.
Many review websites allow a user to enter a summary in addition to a full
review. Intuitively, summary information may give additional benefit for review
sentiment analysis. In this paper, we conduct a study to exploit methods for
better use of summary information. We start by finding out that the sentimental
signal distribution of a review and that of its corresponding summary are in
fact complementary to each other. We thus explore various architectures to
better guide the interactions between the two and propose a
hierarchically-refined review-centric attention model. Empirical results show
that our review-centric model can make better use of user-written summaries for
review sentiment analysis, and is also more effective compared to existing
methods when the user summary is replaced with summary generated by an
automatic summarization system.

本文旨在利用摘要信息实现评论情感分析，探讨不同交互方式并提出一个分类器，结果表明该分类器能更好地利用用户撰写的摘要进行评论情感分析，并且在使用自动摘要系统生成摘要时比现有方法更有效。