MRCBert: A Machine Reading ComprehensionApproach for Unsupervised Summarization
This addresses the challenge for customers in making purchase decisions by providing concise summaries from reviews, though it is incremental as it builds on existing models like BERT.
The authors tackled the problem of summarizing lengthy product reviews by introducing MRCBert, an unsupervised method that uses Machine Reading Comprehension to generate rating-wise and aspect-wise summaries, achieving reasonable performance without domain-specific training data.
When making an online purchase, it becomes important for the customer to read the product reviews carefully and make a decision based on that. However, reviews can be lengthy, may contain repeated, or sometimes irrelevant information that does not help in decision making. In this paper, we introduce MRCBert, a novel unsupervised method to generate summaries from product reviews. We leverage Machine Reading Comprehension, i.e. MRC, approach to extract relevant opinions and generate both rating-wise and aspect-wise summaries from reviews. Through MRCBert we show that we can obtain reasonable performance using existing models and transfer learning, which can be useful for learning under limited or low resource scenarios. We demonstrated our results on reviews of a product from the Electronics category in the Amazon Reviews dataset. Our approach is unsupervised as it does not require any domain-specific dataset, such as the product review dataset, for training or fine-tuning. Instead, we have used SQuAD v1.1 dataset only to fine-tune BERT for the MRC task. Since MRCBert does not require a task-specific dataset, it can be easily adapted and used in other domains.