The future use cases of MDS in real-world applications

In the exponentially increasing digital world, every company does generate far excessive text data. Text summarization is crucial for structuring and analyzing data for businesses from multiple sources to get deeper insights fast and more effectively. Natural language generation technology that instantly transforms a text or a collection of texts within the same subject right into a concise summary that contains crucial semantic information is called Abstractive Multi-Document Summarization.

Multi-Document Summarization technology is a hot topic in the data science world. Once this technology develops, It will automate and abstractly sum up multiple papers to generate a meaningful, accurate, and pertinent recap of a given subject.

Nowadays, almost all Big Tech companies and other startups are working on this technology. At the same time, many AI companies have integrated many other Natural language Generation technologies into real-world applications.

Multi-document Summarization technology has two major parts:

First, Data-To-Text, this technology has widely adopted and deployed in real-world applications. Software applications that instantly transform Charts, Tables, and Graphs data into plain English narratives to discover and get insights from sales, demographics, financial performance, and other numbers. Usually, users can integrate those applications into Business Intelligence tools like Tableau, Microsoft BI, Power BI, Exel, etc.

Second, Text-To-Text is an exciting technology and is currently under development by many players in this field. It is a more complex technology and has more comprehensive use cases. When Text-To-Text Multi-Document Summarization develops, it can be used in various industries and power other Artificial Intelligence subcategories.

The future use cases of MDS in real-world applications:

• Search engines will create more advanced snippets by summarizing multiple text sources to respond to queries and keep users on their pages as long as possible.
• This technology can empower voice assistants and chatbot technologies. A Multi-Document Summarization application could assemble a cohesive answer in real-time by collecting the most relevant text content for a particular question.
• Automate text content analysis to get insights from multiple sources. Text-To-Text MDS tech synthesizes textual content by incorporating logical outcomes with contextualized narratives. Analyzing and structuring text information from multi-document resources can assist entrepreneurs and organizations gain deeper insights into what their clients are seeking.
• Electronic Health Records (EHR) are a modern way to store all patients' data to get access to treat them more efficiently, but doctors don't have time to read and look through all data. Multi-Document Summarization technology could summarize patients' medical history for doctors with a summary of all critical information. This technology will change the way doctors and hospitals understand patients' data.
• Telemedicine technology needs to make the procedure scalable. MDS can be a vital component in the telehealth supply chain when it pertains to examining clinical instances and routing these to the ideal wellness expert.

Also, Text-To-Text MDS technologies can improve other AI/NLP subcategories like meeting summarization, Media monitoring, video scripting, Wiki article creation, Q&A generation from existing documents, and much more.