Business performance assistant
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.
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.
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.
The Brevi assistant is a novel way to summarize, assemble, and consolidate multiple text documents, reports, reviews, feedback, etc.
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