Open AccessText Summarization using Deep Learning
Author(s) -
Saketh Mattupalli*,
Apurva Bhandari,
Badugu Praveena
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a3056.059120
Subject(s) - automatic summarization , computer science , artificial intelligence , natural language processing , focus (optics) , popularity , text graph , deep learning , multi document summarization , information retrieval , desk , psychology , social psychology , physics , optics , operating system
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.