Using automatic or semi-automatic summarization systems enables commercial abstract services to increase the number of texts they are able to process.Personalized summaries are useful in question-answering systems as they provide personalized information.Automatic summarization algorithms are less biased than human summarizers.Automatic summarization improves the effectiveness of indexing.When researching documents, summaries make the selection process easier.In their 2014 book on the subject titled “ Automatic Text Summarization,” the authors provide 6 reasons why we need automatic text summarization tools. We cannot possibly create summaries of all of the text manually there is a great need for automatic methods. Page xix, Automatic Text Summarization, 2014. Processing documents is therefore a perfunctory task, mostly due to the lack of standards. Most of this large volume of documents is unstructured: it is unrestricted and has not been organized into traditional databases. Textual information in the form of digital documents quickly accumulates to huge amounts of data.
There is a great need to reduce much of this text data to shorter, focused summaries that capture the salient details, both so we can navigate it more effectively as well as check whether the larger documents contain the information that we are looking for. The data is unstructured and the best that we can do to navigate it is to use search and skim the results. Think of the internet, comprised of web pages, news articles, status updates, blogs and so much more. There is an enormous amount of textual material, and it is only growing every single day.
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