Different from generative summarization methods， extractive summarization methods are more feasible to implement， more readable， and more widely used. At present， the literatures on extractive summarization methods mostly analyze and review some specific methods or fields， and there is no multi-faceted and multi-lingual systematic review. Therefore， the meanings of text summarization generation were discussed， related literatures were systematically reviewed， and the methods of extractive text summarization based on unsupervised learning and supervised learning were analyzed multi-dimensionally and comprehensively. First， the development of text summarization techniques was reviewed， and different methods of extractive text summarization were analyzed， including the methods based on rules， Term Frequency-Inverse Document Frequency （TF-IDF）， centrality， potential semantic， deep learning， graph sorting， feature engineering， and pre-training learning， etc. Also， comparisons of advantages and disadvantages among different algorithms were made. Secondly， datasets in different languages for text summarization and popular evaluation metrics were introduced in detail. Finally， problems and challenges for research of extractive text summarization were discussed， and solutions and research trends were presented.