• Rasika Deshpande
• Shruti Rothe
• Shivani Devgirikar
• Sneha Sultane
• Vinaya Kulkarni

In this paper, we propose a straightforward component extraction algorithm that can accomplish high archive order precision with regards to advancement driven subjects.Document classification has become an important field of research due to the increase of unstructured text documents available in digital form.Programmed content synopsis turns into a significant method for finding applicable data correctly in huge content in a brief span with little endeavors.In the proposed system,we take PDF as input,classify it according to it''s domain and summarize it using power of Natural Language Processing and Machine Learning.Here we are providing options for selecting required line of summary,the prediction result will send to the user email with summary and classified domain.Having generated summary help us to tell whether to deep dive in detail or not.The main motive of project to save time required in document classification and understanding it.

Naïve Bayes (NB), Machine Learning, Natural Language processing, TF-IDF(Term-Frequency inverse document frequency)

"DOCUMENT CLASSIFICATION AND SUMMARIZER USING PROBABILISTIC CLASSIFIER.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.511-514, April 2020, Available :http://www.jetir.org/papers/JETIR2004565.pdf

"DOCUMENT CLASSIFICATION AND SUMMARIZER USING PROBABILISTIC CLASSIFIER.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp511-514, April 2020, Available at : http://www.jetir.org/papers/JETIR2004565.pdf