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We provide a literature review about Automatic Text Summarization (ATS)systems. We consider a citation-based approach. We start with some popular andwell-known papers that we have in hand about each topic we want to cover and wehave tracked the "backward citations" (papers that are cited by the set ofpapers we knew beforehand) and the "forward citations" (newer papers that citethe set of papers we knew beforehand). In order to organize the differentmethods, we present the diverse approaches to ATS guided by the mechanisms theyuse to generate a summary. Besides presenting the methods, we also present anextensive review of the datasets available for summarization tasks and themethods used to evaluate the quality of the summaries. Finally, we present anempirical exploration of these methods using the CNN Corpus dataset thatprovides golden summaries for extractive and abstractive methods.

A comprehensive review of automatic text summarization techniques: method, data, evaluation and coding