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Some real-life applications of NLP are as follows:
                 • Automatic Text Summarisation: Automatic Text Summarisation is the process of creating the most meaningful
                and relevant summary of voluminous texts from multiple sources. Google News, Blogspot, Inshorts app and
                many other apps are dealing with text summarisation tasks by using Machine learning algorithms that helps in
                producing short and relevant data from the multiple sources, by identifying the important sections in a huge
                textual dataset.
                There are two different approaches of creating Automatic text summarisation:
                o  Extractive summarisation: In this approach, selected text, phrases, sentences or sections are extracted from
                   the multiple sources and joined appropriately to form a concise summary.
                o  Abstractive Summarisation: In this approach, the summary is created by interpreting the text from multiple
                   sources using advanced NLP techniques. This new summary may or may not have text, phrases or sentences
                   from the original documents.

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                 • Autogenerated Captions: NLP is used to produce descriptive text for images, videos or real-world contexts. They
                are widely used in areas such as social media and content creation. The captions are generated using speech-
                to-text and image-to-text features on a real time basis. For example, Captions are automatically generated for
                speech, in the language of the meeting, using Instagram’s or YouTube’s captioning tools.


























                 • Language Translation: It refers to the process of converting text or speech from one language to another using
                NLP. This technology aids in learning new languages and facilitates cross-cultural understanding. The process of
                language translation continues to evolve, with advancements in AI and NLP making it increasingly fast, accurate,
                and accessible. For example, Google Translate uses deep learning models to improve translation quality over
                time.



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