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Our research introduces an innovative Natural Language Generation (NLG)approach that aims to optimize user experience and alleviate the workload ofhuman customer support agents. Our primary objective is to generate informalsummaries for online articles and posts using an offline reinforcement learningtechnique. In our study, we compare our proposed method with existingapproaches to text generation and provide a comprehensive overview of ourarchitectural design, which incorporates crawling, reinforcement learning, andtext generation modules. By presenting this original approach, our paper makesa valuable contribution to the field of NLG by offering a fresh perspective ongenerating natural language summaries for online content. Through theimplementation of Empowering NLG, we are able to generate higher-qualityreplies in the online domain. The experimental results demonstrate asignificant improvement in the average "like" score, increasing from 0.09954378to 0.5000152. This advancement has the potential to enhance the efficiency andeffectiveness of customer support services and elevate the overall userexperience when consuming online content.

Empowering NLG: Offline Reinforcement Learning for Informal Summarization in Online Domains