Text Summarization automatically shortens a set of data computationally, to create a subset that represents the most important or relevant information within the original content. Text summarization finds the most informative sentences in a document. It creates a short, accurate, and fluent summary of any text document.
The need for Text Summarization.
Automatic text summarization is greatly needed to address the ever-growing amount of text data available online to both better help discover relevant information and to consume relevant information faster. Automatic summarization improves the effectiveness of indexing when researching documents, making the selection process easier and less time consuming.
Applications of Text Summarization .
Search Marketing And Seo
When evaluating search queries for SEO, it is critical to have a well-rounded understanding of what your competitors are talking about in their content. Multi-document summarization can be a powerful tool to quickly analyse dozens of search results, understand shared themes and skim the most important points.
Summarization could surface the most important content within email and let us skim emails faster when there is a situation of email overload.
Internal Document Workflow
Summarization can enable analysts to quickly understand everything the company has already done in a given subject, and quickly assemble reports that incorporate different points of view.
Investment banking firms spend large amounts of money acquiring information to drive their decision-making. Summarization systems tailored to financial documents like earning reports and financial news can help analysts quickly derive market signals from content.
Legal Contract Analysis
Summarization systems could be developed to analyse legal documents. A summarizer can add value by condensing a contract to the riskier clauses, or help you compare agreements.