Review of Applications and Issues of Text Summarization

Text summarization deals with the creation of human-readable summaries from text documents and involves syntactic, semantic and discourse-level processing of text. The summarization system reduces a text document and conveys the key meaning of the text. Reducing data helps users to spot the specified information quickly without wasting time and energy in reading the whole document. This is often the foremost challenging task in information retrieval systems. Text summarization approaches are extractive and abstractive. Extractive summarization approaches are intended to come up with summaries by selecting key units (a subset of existing words, phrases, or sentences) of the original document. Abstractive summarization approaches require advanced NLP tools like ideal semantic parsers and language generation systems. supported input type, summarization will be classified into single and multi-document summaries.