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

Abstract

The development of the Internet has made it possible to obtain large amounts of text data. Search engines are used to select text data, but examining all text search results relevant to your query is impossible. Therefore, the only way to summarize content without losing meaning is to use text summarization or automatic text summarization(ATS) in natural language processing (NLP). We have proposed a neural network algorithm to generate title text from Arxiv’s legacy data collections. Recurrent Neural Network (RNN) units and temperature functions are used to create creative content. Google colab is used for experimental setup and result analysis. The results are compared with model accuracy and loss for better analysis.