Text Summarization using Ml and Nlp
Manikanta K.B1, Bhagavath Sai M2, I Venkat3, Sreekar Reddy B4

1Manikanta *, CSE department, GITAM University, Bangalore, India.
2Bhagavath Sai M, CSE department, GITAM University, Bangalore, India.
3Venkat I, CSE department, GITAM University, Bangalore, India.
4Sreekar Reddy B, CSE department, GITAM University, Bangalore, India. 

Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1188-1190 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7278049420/2020©BEIESP | DOI: 10.35940/ijeat.D7278.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Quantity of data produced per day is around 2.5 quintillion bytes. Right now, no one has the time to pursue each and everything. With the growth of technology and digital media, people are becoming very lazy; they are looking for everything more smartly. If they want to read any article or newspaper, they cannot go through every line that has been given. To overcome this problem, an automatic text summarizer using Machine Learning (ML) and Natural Language Processing (NLP) with the python programming language has been introduced. This automatic text summarizer will generate a concise and meaningful summary of the text from resources like textbooks, articles, messages by using a text ranking algorithm. The input text that is given will be split into sentences; these sentences are again converted into vectors. These vectors are represented as a similarity matrix and based on these similarities; matrices sentence rankings will be given. The higher ranked sentences will be the final summary of the given input text.
Keywords: Num Py, Pandas, Machine Learning (ML), Natural Language Processing (NLP), Text Ranking Algorithm