Comparison of Data Augmentation Methods in Pointer–Generator Model
Tomohito Ouchi, Masayoshi Tabuse · Journal of Robotics Networking and Artificial Life · 2021
In this research, we proposed a data augmentation method using topic model for Pointer-Generator model.This method is that adding important sentences to an article as extended article.Furthermore, we compare our proposed method with data augmentation methods using Easy Data Augmentation (EDA), LexRank and Luhn.EDA consists of synonym replacement, random insertion, random swap, and random deletion.LexRank is based on Google's search method and Luhn defines sentence features and ranks sentences.We considered which method is suitable for data augmentation.We confirm that most accurate model is the model using data augmentation method by topic model.