Publication Date

Spring 5-24-2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Robert Chun

Second Advisor

Navrati Saxena

Third Advisor

Mayur Barge

Keywords

Deep learning, Text Encapsulation, system, dataset, features, automated, model, machine learning

Abstract

Data is an important aspect in any form be it communication, reviews, news articles, social media data, machine or real-time data. With the emergence of Covid-19, a pandemic seen like no other in recent times, information is being poured in from all directions on the internet. At times it is overwhelming to determine which data to read and follow. Another crucial aspect is separating factual data from distorted data that is being circulated widely. The title or short description of this data can play a key role. Many times, these descriptions can deceive a user with unwanted information. The user is then more likely to spread this information with his colleagues/family and if they too are unaware, this false piece of information can spread like a forest wildfire. Deep machine learning models can play a vital role in automatically encapsulating the description and providing an accurate overview. This automated overview can then be used by the end user to determine if that piece of information can be consumed or not. This research presents an efficient Deep learning model for automating text encapsulation and its comparison with existing systems in terms of data, features and their point of failures. It aims at condensing text percepts more accurately.

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