Deep Reinforcement Learning for Automated Text Summarization: Innovations and Challenges

Summarizing text automatically is a highly demand-ing task in natural language processing.It has become an indis-pensable activity with the outbreak of so much digital content over the past few years.Even the traditional approaches such as extractive and abstractive summarizations often fail to produce both conciseness and semantic richness in the summaries.The task can be viewed as generating sequence in this new paradigm which DRL offers.Here, a model learns to optimize a variety of reward functions that depend on quality metrics such as ROUGE, relevance, and fluency.This paper discusses recent innovations in DRL for text summarization by highlighting the advancements in policy gradient methods, value-based learning, and actor-critic models.We comment on challenges of sparsity in the reward signal, interpretability of the model, and computational efficiency and give some ideas about where fruitful future research may be.Lastly, we discuss hybrid architectures that integrate DRL with pre-trained models such as GPT and BERT in the belief that this will better improve the coherence and informativeness of summaries.