Comparison of Deep Learning Models for Automatic Generation of Product Description on E-commerce site
Kenji Fukumoto, Rinji Suzuki, Hiroyuki Terada, Masafumi Bato, Akiyo Nadamoto · 2021
People can readily post their products on e-commerce sites. When users present their product on an e-commerce site, they must create a document describing the item and encouraging its purchase. However, it is not easy for beginner users to create a sentence that describes a product. For this study, the sentence describing the product is called the product description. We propose a method for automatically generating the product description based on a comparison of LSTM and GPT-2. Specifically, we examine the data structure of the product included in the existing product description. Then we use data based on these data structures as input to compare these two methods. Furthermore, we conduct two experiments to measure the benefits of our proposed method based on our proposed evaluation index.