Text Summarization using Deep Learning
Saketh Mattupalli1, Apurva Bhandari2, B.J Praveena3
1Saketh Mattupalli, is currently pursuing B.E Degree program in Computer Science and Engineering in Matrusri Engineering College, Saidabad, Affiliated to Osmania University, Hyderabad, Telangana, India.
2Apurva Bhandari is currently pursuing B.E Degree program in Computer Science and Engineering in Matrusri Engineering College, Saidabad, affiliated to Osmania University, Hyderabad, Telangana, India
3B.J Praveena is currently working as a Asst.professor in Matrusri Engineering College, Saidabad, Affiliated to Osmania University, Hyderabad, Telangana, India.
Manuscript received on May 02, 2020. | Revised Manuscript received on May 21, 2020. | Manuscript published on May 30, 2020. | PP: 2663-2667 | Volume-9 Issue-1, May 2020. | Retrieval Number: A3056059120/2020©BEIESP | DOI: 10.35940/ijrte.A3056.059120
Open Access | Ethics and Policies | Cite | Mendeley
© 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: In this century, Artificial Intelligence AI has gained lot of popularity because of the performance of the AI models with good accuracy scores. Natural Language Processing NLP which is a major subfield of AI deals with analysis of huge amounts of Natural Language data and processing it. Text Summarization is one of the major applications of NLP. The basic idea of Text Summarization is, when we have large news articles or reviews and we need a gist of news or reviews with in a short period of time then summarization will be useful. Text Summarization also finds its unique place in many applications like patent research, Help desk and customer support. There are numerous ways to build a Text Summarization Model but this paper will mainly focus on building a Text Summarization Model using seq2seq architecture and TensorFlow API.
Keywords: Text Summarization, Artificial Intelligence, Natural Language Processing, Machine Learning.
Scope of the Article: Deep Learning