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

TL;DR

This paper presents an offline reinforcement learning approach for generating informal summaries of online articles, improving user engagement and support efficiency in online domains.

Contribution

It introduces a novel NLG method combining crawling, reinforcement learning, and text generation modules for better online content summarization.

Findings

Significant increase in 'like' scores from 0.0995 to 0.5000

Higher-quality replies generated in online support scenarios

Enhanced user experience and support efficiency

Abstract

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.