![]() ![]() Since computers lack human knowledge and language capability, it makes automatic text summarization a very difficult and non-trivial task. It is very challenging, because when we as humans summarize a piece of text, we usually read it entirely to develop our understanding, and then write a summary highlighting its main points. ![]() Automatic text summarization is the task of producing a concise and fluent summary without any human help while preserving the meaning of the original text document. This increasing availability of documents has demanded exhaustive research in the NLP area for automatic text summarization. ![]() This volume of text is an inestimable source of information and knowledge which needs to be effectively summarized to be useful. In the big data era, there has been an explosion in the amount of text data from a variety of sources. Moreover, the generation of summaries can be integrated into these systems as an intermediate stage which helps to reduce the length of the document. There are important applications for text summarization in various NLP related tasks such as text classification, question answering, legal texts summarization, news summarization, and headline generation. Since manual text summarization is a time expensive and generally laborious task, the automatization of the task is gaining increasing popularity and therefore constitutes a strong motivation for academic research. Summarization is the task of condensing a piece of text to a shorter version, reducing the size of the initial text while at the same time preserving key informational elements and the meaning of content. ![]()
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