Abstract
Automated Text Summarization (ATS) systems are very significant in many fields in Natural Language Processing (NLP). ATS generates a shorter version of the source text that contains most of the relevant information in the original text which can help users to find the required information they are looking for saving time and resources. Text summarization can be classified according to different criteria. One of these criteria is the number of input documents. Text summarization tasks can be classified into single-document and multi-document summarization. In single-document summarization, the summary of only one document needs to be extracted, while multi-document summarization requires a collection of documents to be extracted.
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