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One under-the-radar area where AI has seen strength is content summarization. In this context, content summarization refers to utilizing the technology to analyze larger bodies of text and then shorten that content into a readable summary. By summarizing documents, organizations can extract essential information and make documents easier to search and analyze.
In addition to summarizing documents, they can also be categorized. Document categorization is a powerful tool that is used for a wide variety of purposes, such as separating contracts from invoices or identifying specific sections within long documents.
With the vast majority of documents at an enterprise, doing this categorizing and summarizing is a task that just simply isn't possible for humans. This is where AI-powered document categorization comes into play. AI is having tremendous impact in more mundane, day-to-day activities such as business processes that help organizations generate, categorize or classify text and documents.
What is document categorization and classification?
AI content summarization uses machine learning to spot and identify patterns in content text. Its first step is to comprehend the document before it can assess what information is going to be of the most use.
In order to extract value from the documents, the system needs to break it down further through categorization. A computer is able to do so through using natural language processing. Machines are able to scan content quickly and sort it into categories based on training data. An example of categorization can be determining if a document is an invoice versus a press release and sorting each document appropriately.
Over the last 20 years much research has been done on automatic document categorization. When dealing with documents, many of these are considered unstructured content, meaning that this data is not organized in a predefined manner. As a result, you can't simply build a computer program to categorize this content but need to employ machine learning.
Document categorization can be difficult because there are so many different types of documents, different writing purposes and different document formats. Because of this difficulty, and the nature of machine learning, we will never have 100% accuracy when it comes to classification; however, some companies are getting close.
Applications of document categorization and AI-based content summarization
Content summarization can be applied to many fields. The need to quickly summarize documents has led companies to adopt AI document summarization tools. Because these two functions often go hand in hand, these tools can also provide document categorization.
The medical field is no stranger to documents and is seeing content summarization being applied to help in various ways. More medical offices and facilities are starting to use the technology to quickly summarize medical documents. These summaries are then used to route the documents to the appropriate medical professional. For example, a document regarding a nose condition would be summarized and then routed to an ear, nose and throat physician.
Marketing companies have been using AI-based document summarization to take long content and condense it down to more digestible content. This short content is much more usable in regard to social media campaigns and accounts. Using AI to summarize these documents allows marketing professionals to perform a task much faster than they could if only humans were involved.
AI-based summarization has found its way into the legal realm as well where it performs initial analysis of legal contracts. Legal documents can be difficult to read due to their complex writing and terminology. The summaries generated by this technology can give an idea of what the document says and better prepare the reader for understanding the document as a whole. It can also be used to determine whether there may be hidden clauses or risks in a document.
Document summarization is also being utilized by search engine companies to pare down documents and provide quick synopses to be displayed in search results. Keywords can also be extracted using document summarization. Similarly, it's also being used by internal search engines at companies and government agencies to help quickly sort through the vast amounts of data they have and retrieve the necessary information for which they are searching.
Moving forward with content summarization
Despite the efforts of researchers, startups and companies, searching documents can still be difficult. Data is constantly being generated at an astounding rate, and most of this data is unstructured. However, the use of AI-enabled content summarization is making a significant impact, giving more visibility and understanding to this information. The ability to use AI-enabled content summarization to analyze documents allows organizations to quickly digest petabytes of data quickly.
AI is enabling their employees to do their jobs more effectively, allowing organizations to handle information they previously lacked the manpower to handle and allowing marketing departments to generate more content than previously. In this way, the application of AI to content summarization will continue to provide value for many industries in the years ahead.