Synthesize, if you do not have: Effective Synthetic Dataset Creation Strategies for Self-Supervised Opinion Summarization in E -commerce (2023)

Abstract: In e-commerce, opinion summarization is the process of condensing the opinions presented in product reviews. However, the absence of large amounts of supervised datasets presents challenges in generating both aspect-specific and general opinion summaries. Existing approaches have attempted to address these challenges through synthetic dataset creation (SDC). However, general opinion summarization models struggle to generate summaries faithful to the input reviews whereas aspect-specific opinion summarization models are limited due to their reliance on human-specified aspects and seed words. To address this, we propose SDC strategies tailored for general and aspect-specific opinion summarization. We experimented on three e-commerce test sets: Oposum+, Amazon, and Flipkart. For general opinion summarization, pre-trained language model (PLM) fine-tuned on our general synthetic dataset surpass the SOTA on average by 2.3 R1 points. Faithfulness evaluation metrics and human evaluations indicate that our model-generated summaries are more faithful to the input compared to others. For aspect-specific opinion summarization, PLM fine-tuned on our aspect-specific synthetic dataset surpass SOTA by ~ 1 R1 point without the aid of any human-specified aspects or seed words.

## introduction in the e-commerce domain, customer reviews are essential for making informed buying decisions. however, going through all the product reviews before making a decision is overwhelming. opinion summarization is the process of summarizing the opinions presented in the reviews @xcite @xcite . however, it is infeasible and expensive to obtain reference summaries at a large scale for general and aspect-specific opinion summarization. ## results and analysis general opinion summarization. we use the rouge-{1,2,l} f1 score @xcite ) (r1, r2 & rl) to assess the generated summary quality. ## conclusion and future work we proposed synthetic dataset creation approaches for general and aspect-specific opinion summarization. for general opinion summarization, our sdc approach enables models to generate summaries that are more faithful to the input than existing approaches, as indicated through automatic and human evaluations. for aspect-specific opinion summarization, our sdc approach enabled models to outperform existing models without relying on any human-specified aspects or seed words, making our approach generalizable to other opinion summarization domains where such human-specified aspects or seed words are not defined. we plan to further extend our work to domains other than e-commerce for opinion summarization.