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NLP and ML are two popular subfields of Artificial Intelligence that are being integrated into numerous online tools for better performance.
One of those tools includes text summarizers. They are used by teachers, students, writers, researchers, and many more to quickly condense lengthy pieces of text into concise ones without altering the original meaning. After the integration of NLP and ML, summarizing tools are now referred to as “AI-powered summarizing tools.” This means they are now quicker, more accurate, and more efficient.
However, one question that arises here is how both the NLP and ML contribute to enhancing the working efficiency of summarization tools. To get the answer to this question, read this blog till the end.
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NLP and ML are two popular subfields of Artificial Intelligence that are being integrated into numerous online tools for better performance.
One of those tools includes text summarizers. They are used by teachers, students, writers, researchers, and many more to quickly condense lengthy pieces of text into concise ones without altering the original meaning. After the integration of NLP and ML, summarizing tools are now referred to as “AI-powered summarizing tools.” This means they are now quicker, more accurate, and more efficient.
However, one question that arises here is how both the NLP and ML contribute to enhancing the working efficiency of summarization tools. To get the answer to this question, read this blog till the end.
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