A Survey On Deep Learning Based Various Methods Analysis Of Text Summarization

Due to the extremely huge amount of text available on the internet today, there is a need for a method that helps us gather concise and quality information according to our query. People expressing theirviews on social media, product reviews by customers, news articles, blogs, etc. are some sources from where text arises. There are two ways of summarization (SUMZ): abstractive (ABSR) and extractive (EXTR). It can be achieved using various methods like deep learning; Neural Networks (NN), fuzzy C-means clustering etc. and these methods can be either supervised or unsupervised. Moreover, SUMZ can be achieved keeping in mind the user's emotions and views. Researchers are performing experiments on various datasets like views from social media, product reviews, news articles, or any other source online and propose various solutions to produce the best of the "gold summaries (summ.)" that are fair to the original piece of text. In this paper, we have made an attempt to study the various methods that are used for text SUMZ and observe the trends, the developments, the accomplishments and we explore new dimensions for future work to be done in this expanding field.