A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articles based on Deep Learning


Sherif Kamel Hussein1, 2, and Joza Nejer AL-Otaibi2, 1October University for Modern Sciences and Arts, Egypt, 2Arab East Colleges for Graduate Studies, KSA


In this new era, where tremendous information is available on the internet, it is of most important to provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of large documents of text. Therefore, there is a problem of searching for relevant documents from the number of documents available, and absorbing relevant information from it. In order to solve the above two problems, the automatic text summarization is very much necessary. Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. This Paper introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on deep learning.


Abstractive Text Summarization, Natural Language Processing, Extractive Summarization, Automatic Text Summarization, Machine Learning