A Survey on Recent Text Summarization Techniques

Abstract

NLP (Natural Language Processing) is a subfield of artificial intelligence that examines the interactions between computers and human languages, specifically how to design computers to process and evaluate vast quantities of natural language data. The procedure of condensing long text into paragraphs or phrases is known as NLP text summarization. This technique retrieves essential information from a text while keeping its meaning. This decreases the time necessary to comprehend large elements, such as articles, without compromising the integrity of the content. Major difficulties in text summarizing include subject identification, interpretation, summary construction, and summary evaluation. Most real-world systems that summarize texts rely on extractive summarization. Hence, there must be a way to summarize lengthy assessments into concise statements with few words that convey the same information. The use of text summarization in this context can be helpful. Text Summarization is of interest to several researchers in natural language processing. This study provides an overview of the different text-summarization approaches used in Natural language processing.