A Comprehensive Study on Text Summarization using Deep Learning

Abstract

The internet revolution has resulted in an exponential increase in the volume of digital material available on the web. With the increasing rise of online platforms such as social networking sites, e-commerce websites, blogs, forums, and other digital services, people all over the world are continuously participating in virtual interactions. These platforms have evolved into critical communication, marketing, and collaboration tools, resulting in the everyday collection of massive amounts of textual data. If this data is properly processed, it can provide useful insights that can have a substantial impact on business strategy and promote industrial progress. In this context, deep learning has emerged as an effective method for making sense of unstructured textual data. Text summarization, sentiment analysis, and topic modeling are becoming increasingly popular in a variety of applications. Among these, text summarizing with deep learning frameworks stands out for its capacity to condense enormous amounts of information into short, relevant summary. This not only saves time, but also facilitates faster decision-making processes. This paper seeks to provide a complete review of deep learning-based text summarizing approaches. It investigates current industry trends, assesses the efficacy of existing tools, and identifies critical research gaps that must be addressed in order to develop the discipline forward