Empowering NLG: Offline Reinforcement Learning for Informal
  Summarization in Online Domains
Zhi-Xuan Tai Po-Chuan Chen
Our research introduces an innovative Natural Language Generation (NLG)
approach that aims to optimize user experience and alleviate the workload of
human customer support agents. Our primary objective is to generate informal
summaries for online articles and posts using an offline reinforcement learning
technique. In our study, we compare our proposed method with existing
approaches to text generation and provide a comprehensive overview of our
architectural design, which incorporates crawling, reinforcement learning, and
text generation modules. By presenting this original approach, our paper makes
a valuable contribution to the field of NLG by offering a fresh perspective on
generating natural language summaries for online content. Through the
implementation of Empowering NLG, we are able to generate higher-quality
replies in the online domain. The experimental results demonstrate a
significant improvement in the average "like" score, increasing from 0.09954378
to 0.5000152. This advancement has the potential to enhance the efficiency and
effectiveness of customer support services and elevate the overall user
experience when consuming online content.