  Recently, opinion summarization, which is the generation of a summary from
multiple reviews, has been conducted in a self-supervised manner by considering
a sampled review as a pseudo summary. However, non-text data such as image and
metadata related to reviews have been considered less often. To use the
abundant information contained in non-text data, we propose a self-supervised
multimodal opinion summarization framework called MultimodalSum. Our framework
obtains a representation of each modality using a separate encoder for each
modality, and the text decoder generates a summary. To resolve the inherent
heterogeneity of multimodal data, we propose a multimodal training pipeline. We
first pretrain the text encoder--decoder based solely on text modality data.
Subsequently, we pretrain the non-text modality encoders by considering the
pretrained text decoder as a pivot for the homogeneous representation of
multimodal data. Finally, to fuse multimodal representations, we train the
entire framework in an end-to-end manner. We demonstrate the superiority of
MultimodalSum by conducting experiments on Yelp and Amazon datasets.
