{"id":7855,"date":"2023-01-25T14:24:18","date_gmt":"2023-01-25T19:24:18","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?page_id=7855"},"modified":"2025-04-15T12:52:27","modified_gmt":"2025-04-15T16:52:27","slug":"juan-junco","status":"publish","type":"page","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/students\/2023-2\/cs488\/juan-junco\/","title":{"rendered":"Juan Junco"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n\n\n\n<p>Identifying the most relevant text information from online texts has become a standard task now that data is easily accessible. Conse- quently, summary generation has gained significant importance by reducing the amount of text a person has to read to get meaningful information from the text. The underlying field for this process Natural Language Processing (NLP). NLP enables algorithms to understand and rewrite a text based on the original information. In this proposal, I will explore how a Convolutional Neural Network (CNN) can create a new approach for visualizing the critical aspects of a text. Instead of generating a shorter text, the output will be a version with the essential elements highlighted.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Poster<\/h2>\n\n\n\n<div class=\"wp-block-file\"><a href=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/04\/jejunco20_Epic_Poster-3.pdf\">jejunco20_Epic_Poster-3<\/a><a href=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/04\/jejunco20_Epic_Poster-3.pdf\" class=\"wp-block-file__button\" download>Download<\/a><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Video<\/h2>\n\n\n\n<figure class=\"wp-block-embed-youtube wp-block-embed is-type-video is-provider-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Capstone\" width=\"940\" height=\"529\" src=\"https:\/\/www.youtube.com\/embed\/XeVUefpua4o?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Paper<\/h2>\n\n\n\n<div class=\"wp-block-file\"><a href=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Proposal-9.pdf\">Paper<\/a><a href=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Proposal-9.pdf\" class=\"wp-block-file__button\" download>Download<\/a><\/div>\n\n\n\n<h2 class=\"wp-block-heading\">Architecture Diagram<\/h2>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"587\" src=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Blank-diagram-3-1024x587.png\" alt=\"\" class=\"wp-image-8356\" srcset=\"https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Blank-diagram-3-1024x587.png 1024w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Blank-diagram-3-300x172.png 300w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Blank-diagram-3-768x440.png 768w, https:\/\/portfolios.cs.earlham.edu\/wp-content\/uploads\/2023\/05\/Blank-diagram-3-1536x881.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Gitlab: <a href=\"https:\/\/code.cs.earlham.edu\/jejunco20\/capstone\" data-type=\"URL\" data-id=\"https:\/\/code.cs.earlham.edu\/jejunco20\/capstone\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/code.cs.earlham.edu\/jejunco20\/capstone<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Abstract Identifying the most relevant text information from online texts has become a standard task now that data is easily accessible. Conse- quently, summary generation has gained significant importance by reducing the amount of text a person has to read &hellip; <a href=\"https:\/\/portfolios.cs.earlham.edu\/index.php\/students\/2023-2\/cs488\/juan-junco\/\">Read More<\/a><\/p>\n","protected":false},"author":128,"featured_media":0,"parent":5058,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-7855","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Juan Junco - CS\/DS Student Portfolios<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/portfolios.cs.earlham.edu\/index.php\/students\/2023-2\/cs488\/juan-junco\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Juan Junco - CS\/DS Student Portfolios\" \/>\n<meta property=\"og:description\" content=\"Abstract Identifying the most relevant text information from online texts has become a standard task now that data is easily accessible. 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