A text is a set of words conveying a particular semantic based on their order, representation and structure. Those elements can be associated through a different set of interpretations, based on frequency and proportionality. The problem with context is that numbers do not help understand the semantics and fall short to convey the message of the text. The graphical representation of text semantics focuses on the conversion of text to images. Contrarily to word clouds that simply produce frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact the semantic and the context of the words in the text. They provide a deeper understanding and can be better interpreted. Models such as AttnGAN already exist to convert text into images with a certain level of success, but there has not been work done concerning the conversion of long and complex texts in an image or a set of images. The goal of this analysis is to first, provide an understanding of how we divide the text in bits that improve the resulting image and how does the summarization methodology affect the image result.
","abstract_html":"<p>A text is a set of words conveying a particular semantic based on their order, representation and structure. Those elements can be associated through a different set of interpretations, based on frequency and proportionality. The problem with context is that numbers do not help understand the semantics and fall short to convey the message of the text. The graphical representation of text semantics focuses on the conversion of text to images. Contrarily to word clouds that simply produce frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact the semantic and the context of the words in the text. They provide a deeper understanding and can be better interpreted. Models such as AttnGAN already exist to convert text into images with a certain level of success, but there has not been work done concerning the conversion of long and complex texts in an image or a set of images. The goal of this analysis is to first, provide an understanding of how we divide the text in bits that improve the resulting image and how does the summarization methodology affect the image result.</p>","abstract_has_math":false,"creators":["Tiba Fossoh, Karl Kevin"],"institution":null,"degree_name":"Master of Science in Computer Science (MSCS)","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Dan Lo","Coskun Cetinkaya","Junggab Son"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2020,"date_issued":"2020-05-18T07:00:00Z","date_published":"2020-05-18T07:00:00Z","updated_at":"2026-07-24T02:43:42Z","subjects":["Summarization","LSTM","GAN","LDA","Image Realism","Coherence","Graphics and Human Computer Interfaces","Numerical Analysis and Scientific Computing","Theory and Algorithms"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.kennesaw.edu/cs_etd/43","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dan Lo","Coskun Cetinkaya","Junggab Son"]},{"key":"dc:creator","label":"Author","values":["Tiba Fossoh, Karl Kevin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-11-17T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Computer Science (MSCS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Summarization","LSTM","GAN","LDA","Image Realism","Coherence","Graphics and Human Computer Interfaces","Numerical Analysis and Scientific Computing","Theory and Algorithms"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.kennesaw.edu/cs_etd/43"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A text is a set of words conveying a particular semantic based on their order, representation and structure. Those elements can be associated through a different set of interpretations, based on frequency and proportionality. The problem with context is that numbers do not help understand the semantics and fall short to convey the message of the text. The graphical representation of text semantics focuses on the conversion of text to images. Contrarily to word clouds that simply produce frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact the semantic and the context of the words in the text. They provide a deeper understanding and can be better interpreted. Models such as AttnGAN already exist to convert text into images with a certain level of success, but there has not been work done concerning the conversion of long and complex texts in an image or a set of images. The goal of this analysis is to first, provide an understanding of how we divide the text in bits that improve the resulting image and how does the summarization methodology affect the image result.
"]},{"key":"dc:title","label":"Title","values":["Graphical Representation of Text Semantics"]}]}],"canonical_facts":{"dc:contributor":["Dan Lo","Coskun Cetinkaya","Junggab Son"],"dc:creator":["Tiba Fossoh, Karl Kevin"],"dc:date.available":["2020-11-17T08:00:00Z"],"dc:description.abstract":["A text is a set of words conveying a particular semantic based on their order, representation and structure. Those elements can be associated through a different set of interpretations, based on frequency and proportionality. The problem with context is that numbers do not help understand the semantics and fall short to convey the message of the text. The graphical representation of text semantics focuses on the conversion of text to images. Contrarily to word clouds that simply produce frequency mapping of words within the text and topic models that essentially give context to word frequencies and proportionalities, images keep intact the semantic and the context of the words in the text. They provide a deeper understanding and can be better interpreted. Models such as AttnGAN already exist to convert text into images with a certain level of success, but there has not been work done concerning the conversion of long and complex texts in an image or a set of images. The goal of this analysis is to first, provide an understanding of how we divide the text in bits that improve the resulting image and how does the summarization methodology affect the image result.
"],"dc:identifier":["https://digitalcommons.kennesaw.edu/cs_etd/43"],"dc:subject":["Summarization","LSTM","GAN","LDA","Image Realism","Coherence","Graphics and Human Computer Interfaces","Numerical Analysis and Scientific Computing","Theory and Algorithms"],"dc:title":["Graphical Representation of Text Semantics"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science in Computer Science (MSCS)"]},"updated_at":"2026-07-24T02:43:42Z"}