PoBRL: Optimizing Multi-Document Summarization by Blending Reinforcement
  Learning Policies
  We propose a novel reinforcement learning based framework PoBRL for solving
multi-document summarization. PoBRL jointly optimizes over the following three
objectives necessary for a high-quality summary: importance, relevance, and
length. Our strategy decouples this multi-objective optimization into different
subproblems that can be solved individually by reinforcement learning.
Utilizing PoBRL, we then blend each learned policies together to produce a
summary that is a concise and complete representation of the original input.
Our empirical analysis shows state-of-the-art performance on several
multi-document datasets. Human evaluation also shows that our method produces
high-quality output.
