  We present a novel architectural scheme to tackle the abstractive
summarization problem based on the CNN/DMdataset which fuses Reinforcement
Learning (RL) withUniLM, which is a pre-trained Deep Learning Model, to solve
various natural language tasks. We have tested the limits of learning
fine-grained attention in Transformers to improve the summarization quality.
UniLM applies attention to the entire token space in a global fashion. We
propose DR.SAS which applies the Actor-Critic (AC) algorithm to learn a dynamic
self-attention distribution over the tokens to reduce redundancy and generate
factual and coherent summaries to improve the quality of summarization. After
performing hyperparameter tuning, we achievedbetter ROUGE results compared to
the baseline. Our model tends to be more extractive/factual yet coherent in
detail because of optimization over ROUGE rewards. We present detailed error
analysis with examples of the strengths and limitations of our model. Our
codebase will be publicly available on our GitHub.
