Deep Learning Approaches for Document Summarization: An Insight

In the quickly-expanding information age of today, a document summarizing has evolved into an essential and significant tool for assisting with the interpretation of text material. There is a multitude of text content available online, yet it is incredibly difficult for humans to physically summarize huge text pages. Document summarization is a field of study that aims to condense long texts into manageable chunks while preserving key points. Document summaries that are abstract or extractive are employed. The summary is chosen from significant text sentences using extractive summarization techniques. that strategy only used phrases from the original text. The goal of abstractive summarizing techniques is to human-likely paraphrase significant information. Convolutional neural networks (CNN), transformers, fuzzy logic, reinforcement learning, and neural networks are a few examples related to deep learning approaches which may be utilized for text summarization. The research trend in text summarizing has barely altered over the last three years with the introduction to the new trends which leads to the enhancement or how can be increased the effectiveness of text summaries to attain a much perfect and efficient accuracy. This paper analyses many deep learning-based texts summarizing methods that have been created throughout the years as well as contemporary developments in deep learning.