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This news source to serve many purposes in society is increasingly interested by people. This information can be personal blog posts, comments or autobiography of an individual. However, most of these articles are often untitled, or will be assigned by editors during compilation. With the goal of coming up with impressive titles, often the author will choose words that never appear in the original passage, and this is a huge challenge for previous techniques. Stemming from this need to automatically title, we will introduce and proposed the PGN-LM model, a system capable of automatically summarizing and titleing text. The architecture of the PGNLM model is built by combining modern natural language processing models, which stands out for its ability to create impressive titles, with words that have never been seen before appear in the original text. We tested our approach with real data and got positive results, based on algorithmic automated evaluation and manual human evaluation. In this thesis, Chapter 1 will introduce an overview of the research topic, present the reason for the birth of the PGN-LM model and introduce the problem of automatic heading. Next, Chapter 2 will examine the research works related to this topic, analyze the advantages and disadvantages of the approaches to come up with a suitable solution for the problem. Chapter 3 will present the relevant theoretical foundations used to build the PGN-LM system. Chapter 4 aims to present and analyze the PGN-LM model in detail. Continuing with Chapter 5, we will show the process of implementing the topic, including preparing the data set, and providing methods to evaluate the accuracy of the PGN-LM model. 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