• Ritik Pandey
• Dhiren Parekh
• Nutan Thakare
• Vishakha Shelke

At this present time where daily information is produced on the internet. With this, the quantity of information in the form of documents, articles, blogs, etc is increasing daily. The international data corporation analyzed that the total amount of data moving across the globe would increase from 4.4 zettabytes in 2013 to hit 180 zettabytes in 2025. An average person spends 30 to 40 minutes reading news or getting information from the internet. So it is necessary to get summarized information from the original data while containing the most important sentences. By this, a lot of reading time can be saved. Automatic text summarization is the process of creating a shorter form of original data using a machine. We are taking the input in text and pre-processing it by removing blank space etc. After that different token is allocated to different sentences. These tokenized sentences are further given the sentences vector to generate weights of words using the TF - IDF algorithm. Lastly, the sentences with the higher score will be taken out as a summary.

Text summarization, Natural Language Processing, TF-IDF.

"Text Summarization: Providing a Summary of any given input in a different language", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.c608-c612, April-2022, Available :http://www.jetir.org/papers/JETIR2204286.pdf

"Text Summarization: Providing a Summary of any given input in a different language", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppc608-c612, April-2022, Available at : http://www.jetir.org/papers/JETIR2204286.pdf