Evolution of User Specific Summarization Techniques: A Need

Abstract

Ever-growing World Wide Web has created a nuclear effect on accessible data or information in digital content. In the context of available enormous textual information, the problem being going through all the information for quenching once need for relevant and useful data is simply an unfeasible and time-consuming task. For addressing this problem text summarization and text classification came into existence for generating coherent information. Text classification plays a vital role in structuring user specific summary. Widespread research has been done in an automatic text summarization approach where most of summarization techniques fail the key aspect of the user’s specific requirements like user knowledge quotient, profession, proficiency in language, etc. The review in this paper is focused upon diverse state-of-the-art approaches for the summary generation with a broad overview of their efficacy of idea and core concepts, effectiveness, and drawbacks. This article puts special emphasis on the issue of the User Specific Summary problem and then finally concludes by addressing why it is of utmost importance to generate user specific summaries and various methodologies that can be utilized. Finally, to discover the auspicious areas of future improvement, this paper offers insight into numerous open challenges and research strategies.