Combined Objective Function in Deep Learning Model for Abstractive Summarization

Abstract

Abstractive Summarization is the specific task in text generation whose popular approaches are based on the strength of Recurrent Neural Network. With the purpose to take advantages of Convolution Neural Network in text representation, we propose to combine these above networks in our encoder to capture both the global and local features from the input documents. Simultaneously, our model also integrates the reinforced mechanism with the novel reward function to get the closer direction between the learning and evaluating process. Through the experiments in CNN/Daily Mail, our models gains the significant results. Especially, in ROUGE-1 and ROUGE-L, it outperforms the previous works in this task with the expressive improvement (39.09% in ROUGE-L F1-score).