Survey Paper: Automatic Title Generation for Text with RNN and Pre-trained Transformer Language Model

Abstract

Abstract: Nowadays huge amounts of text data are available due to the evolution of the Internet. Although search engines are used to select text data, it is unfeasible to go through the entire search results of text that are related to search intent. Therefore, text summarization is the only method by which we can reduce text data without the loss of information. A new method for Title or Text generation is the Transformer language model that has been trained i.e. GPT-2, GPTNeo, Chat-GPT, and LSTM Model of RNN by which we can generate catchy titles to take readers’ attention and attract them to read a full article. This paper has discovered recent literature on all the previously mentioned topics which are related to Title or Text generators and examines several methods which propose to make use of various language models. The suggested approaches are also contrasted with one another to highlight their unique advantages, and to suggest constantly better ways.