Graduation Semester and Year
2020
Language
English
Document Type
Thesis
Degree Name
Master of Science in Computer Engineering
Department
Computer Science and Engineering
First Advisor
Elizabeth Diaz
Abstract
Research papers and journals have always played a crucial role in the field of research and development. However, these research papers usually have a complex usage of language which limits the range of target readers. The language and the terms used in these literary works can make the concept or the topic tough to understand for a naive reader. The goal of this project is to simplify a complex piece of literature into something meaningful without sounding verbose. The idea is based upon Nobel Prize-winning physicist Richard Feynman’s learning technique known as the Feynman Technique that emphasizes the usage of words that are simple to understand. Such simplification can help a researcher gain more information from more literature in less amount of time. The proposed system can also be used to simplify other text documents apart from research papers and journals. This novel model based on simplicity can be considered a new learning model.
Keywords
Summarization, Extractive summariztion, Simplification, Text mining
Disciplines
Computer Sciences | Physical Sciences and Mathematics
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Vaghasiya, Nilav Bharatkumar, "Extractive Summarization and Simplification of Scholarly Literature" (2020). Computer Science and Engineering Theses. 482.
https://mavmatrix.uta.edu/cse_theses/482
Comments
Degree granted by The University of Texas at Arlington