ELTS: A Brief Review for Extractive Learning-Based Text Summarizatoin Algorithms

Automatically capturing the main points from a single document or multiple documents is a challenging requirement. Extractive text summarization which refers to providing a brief summary extract significant sentences from text, deals with several issues. Recently a considerable amount of work has considered learning approaches as text summarization solutions. Intensive researches have surveyed different strategies for text summarization. This paper influenced by the merit performance of learning methods for this task, analytically reviewed current algorithms. In this paper we suggest a framework called "ELTS" including classification of existing learning based algorithm, introducing several criteria in order to make comparison between current models and an analysis based on these criteria. We offer ELTS with the aim to enhance future research which attempts to a) solve current methods defects, b) employ existing strategies according to their requirements or c) make analytical comparison between current and future work.