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.
翻译:当进行在线采购时,客户必须仔细阅读产品审查,并在此基础上作出决定。但是,审查可能冗长,可能包含重复的信息,有时是不相关的、无助于决策的信息。在本文中,我们引入了MRCBert,这是从产品审查中产生摘要的一种新的不受监督的方法。我们利用机器阅读理解,即MRC,从审查中提取相关意见并产生分数和分数摘要的方法。我们通过MRCBert显示,我们可以利用现有模型和转移学习取得合理的业绩,而这种模型和转移学习对于在有限或低资源情景下学习是有用的。我们在审查亚马逊审查数据集电子产品类别的产品时展示了我们的结果。我们的方法没有受到监督,因为它并不需要任何特定领域的数据集,例如产品审查数据集,用于培训或微调。相反,我们使用SQUAD v1.1数据集只是为了微调BERT,用于MRC任务。由于MCBBERt不需要任务特定的数据集,所以它很容易调整,在其他领域使用。