In e-commerce, opinion summarization is the process of summarizing the
consensus opinions found in product reviews. However, the potential of
additional sources such as product description and question-answers (QA) has
been considered less often. Moreover, the absence of any supervised training
data makes this task challenging. To address this, we propose a novel synthetic
dataset creation (SDC) strategy that leverages information from reviews as well
as additional sources for selecting one of the reviews as a pseudo-summary to
enable supervised training. Our Multi-Encoder Decoder framework for Opinion
Summarization (MEDOS) employs a separate encoder for each source, enabling
effective selection of information while generating the summary. For
evaluation, due to the unavailability of test sets with additional sources, we
extend the Amazon, Oposum+, and Flipkart test sets and leverage ChatGPT to
annotate summaries. Experiments across nine test sets demonstrate that the
combination of our SDC approach and MEDOS model achieves on average a 14.5%
improvement in ROUGE-1 F1 over the SOTA. Moreover, comparative analysis
underlines the significance of incorporating additional sources for generating
more informative summaries. Human evaluations further indicate that MEDOS
scores relatively higher in coherence and fluency with 0.41 and 0.5 (-1 to 1)
respectively, compared to existing models. To the best of our knowledge, we are
the first to generate opinion summaries leveraging additional sources in a
self-supervised setting.

在电子商务中，通过引入额外的信息源和自主训练的方法，我们提出了一种新颖的综合数据集创建策略来生成观点摘要。我们的模型采用了多编码器解码器框架，针对每个信息源使用一个编码器以在生成摘要时实现有效信息选择。实验证明，我们的方法与目前的最佳模型相比，在 ROUGE-1 F1 指标上平均提高了 14.5%。此外，人工评估结果显示，MEDOS 模型在连贯性和流畅性方面相对较高，分别为 0.41 和 0.5（-1 到 1 的评分），相较于现有模型具有更高的质量。据我们所知，我们是第一个利用额外信息源进行自主训练生成观点摘要的研究。