Automatic Text Summarization Using Reinforcement Learning with Embedding Features

An automatic text summarization system can automatically generate a short and brief summary that contains a main concept of an original document. In this work, we explore the advantages of simple embedding features in Reinforcement leaning approach to automatic text summarization tasks. In addition, we propose a novel deep learning network for estimating Qvalues used in Reinforcement learning. We evaluate our model by using ROUGE scores with DUC 2001, 2002, Wikipedia, ACL-ARC data. Evaluation results show that our model is competitive with the previous models.