Product Description and QA Assisted Self-Supervised Opinion
  Summarization
Tejpalsingh Siledar Rupasai Rangaraju Sankara Sri Raghava Ravindra Muddu Suman Banerjee Amey Patil Sudhanshu Shekhar Singh Muthusamy Chelliah Nikesh Garera Swaprava Nath Pushpak Bhattacharyya
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.