Text Summarization: An Essential Study

Abstract

The proliferation of data from diverse sources makes humans insufficient in utilizing the knowledge properly at some instance. To quickly have an overview of abundant information, Text Summarization (TS) comes into play. TS will effectively extract the candidate sentences from the source and represent the saliency of whole knowledge. Over the decades Text Summarization techniques have been transformed by the usage of linguistics to advanced machine learning models, this study explores summarization approaches along with their recent state-of-art models in single and multi-document summarization. This survey is intended to make an extensive study from features representation to sentence selection and summary generation using machine learning, recent graph and evolutionary based methods. The overall investigation will help the researchers to effectively handle large quantities of data in building effective Natural Language Processing applications. Eventually, this study draws popular abstractive mechanisms and observations that would be helpful for the intended research.