mlsanityData to 14 Oct 2026

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

arXiv 2306.17174 ↗First posted 17 Jun 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 57 references that are in the map. First posted on arXiv 17 Jun 2023.

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30 of 57 references.

References · 30

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18 more
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  6. 2023The Impact of Preference Agreement in Reinforcement Learning from Human Feedback: A Case Study in Summarization
  7. 2019Fine-Tuning Language Models from Human Preferences
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