Text Summarization Using Natural Language Processing

With the advancements in the technology, most of the things in this world have become automated. The concept of text summarization came into limelight as summarization of text manually has become a tough and time-consuming task. So, the main purpose of text summarization is to overcome the difficulties faced during manual summarization of text documents or other information from various sources. Text summarization is the process of extracting the main idea of the context or the text and briefly explaining about the context. This process is not only to extract key idea and phrases from the text sources but also generating meaningful summary in a concise and crisp way. The demand for text summarization is raising nowadays because of the large amounts of data from multiple sources like Internet, Twitter, Facebook, Instagram, research papers, and other news articles. Text summarization can be efficiently implemented using NLP as it has many packages and methods in Python or R. Text summarization is also related to text mining as summary is generated based on classifying the given input text. There are different approaches for text summarization and some algorithms are identified to implement these approaches. In this paper, unsupervised learning approach is implemented and cosine similarity technique is used to find the similarity between sentences. To generate rank based on similarity, text rank algorithm is used and sentences with top rank are placed in summarized text.