Knowledge Enhanced Pre-trained Language Model for Product Summarization

Automatic summarization has been successfully applied to many scenarios such as news and information services, assisted recommendations, etc. E-commerce product summarization is also a scenario with great economic value and attention, as they can help generate text that matches the product information and inspires users to buy. However, existing algorithms still have some challenges: the generated summarization produces incorrect attributes that are inconsistent with original products and mislead users, thus reducing the credibility of e-commerce platforms. The goal of this paper is to enhance product data with attributes based on pre-trained models that are trained to understand the domain knowledge of products and generate smooth, relevant and faithful text that attracts users to buy.