An overview on extractive text summarization

With the increasing of online information and recourse texts, text summarization has become an essential and more favorite domain to preserve and show the main purpose of textual information. It is very difficult for human beings to summarize manually large documents of text. Text summarization is the process of automatically creating and condensing form of a given document and preserving its information content source into a shorter version with overall meaning. Nowadays text summarization is one of the most favorite research areas in natural language processing and could attracted more attention of NLP researchers. There are also much more close relationships between text mining and text summarization. According to difference requirements summary with respect to input text, established summarization systems should be created and classified based on the type of input text. In this study, at first, the topic of text mining and its relationship with text summarization are considered. Then a review has been done on some of the summarization approaches and their important parameters for extracting predominant sentences, identified the main stages of the summarizing process, and the most significant extraction criteria are presented. Finally, the most fundamental proposed evaluation methods are considered.