Extractive Document Summarization with Advanced Deep Reinforcement Learning

Due to the exponential increase in the amount of information available on the internet, there has been an increasing interest in automatic text summarization in recent years. The goal of extractive text summarization is to generate a condensed version of a document while keeping its important points. It involves selecting the most relevant sentences from the document and arranging them in a coherent and concise manner. Using Deep Q-Networks (DQN), we provide a novel method for extracting text summarization in this research. In our suggested method, each phrase in the text is given a score depending on its importance to the summary, and the process of summarizing is seen as a sentence ranking problem. The DQN-based model is trained to learn the ranking function, where the reward signal is determined by the extent to which the resulting summary resembles the ground truth summary. We conduct experiments on the CNN/DailyMail dataset, which is a widely-accepted benchmark in this field, and compare our model with state-of-the-art extractive summarization systems and demonstrate that our approach outperforms them in terms of both automatic metrics and human evaluation. Our study highlights the potential of DQN-based models to significantly enhance the quality of extractive text summarization. Our findings suggest that our proposed approach provides a promising avenue for future research in the field of extractive text summarization.