{"id":{"repo_id":"uwo","oai_identifier":"oai:uwo.scholaris.ca:20.500.14721/31025"},"canonical_url":"https://search.dev.ndltd.org/etd/uwo/oai:uwo.scholaris.ca:20.500.14721/31025","repository":{"repo_id":"uwo","name":"Western University","base_url":"https://uwo.scholaris.ca/server/oai/request"},"display":{"title":"Improving Reader Motivation with Machine Learning","abstract":"This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success. There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the best of our knowledge, this is the first work to explicitly address the broad and meaningful impact that NLP and ML can have on the reading experience. In particular, the aim of this thesis is to explore new approaches to supporting internal reading motivation, which is influenced by readability, situational interest, and personal interest. This is performed by identifying new or existing NLP tasks which can address reader motivation, designing novel machine learning approaches to perform these tasks, and evaluating and examining these approaches to determine what they can teach us about the factors of reader motivation. In executing this research, we make use of concepts from NLP such as textual coherence, interestingness, and summarization. We additionally use techniques from ML including supervised and self-supervised learning, deep neural networks, and sentence embeddings. This thesis, presented in an integrated-article format, contains three core contributions among its three articles. In the first article, we propose a flexible and insightful approach to coherence estimation. This approach uses a new sentence embedding which reflects predicted position distributions. Second, we introduce the new task of pull quote selection, examining a spectrum of approaches in depth. This article identifies several concrete heuristics for finding interesting sentences, both expected and unexpected. Third, we introduce a new interactive summarization task called HARE (Hone as You Read), which is especially suitable for mobile devices. Quantitative and qualitative analysis support the practicality and potential usefulness of this new type of summarization.","abstract_html":"This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success. There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the best of our knowledge, this is the first work to explicitly address the broad and meaningful impact that NLP and ML can have on the reading experience. In particular, the aim of this thesis is to explore new approaches to supporting internal reading motivation, which is influenced by readability, situational interest, and personal interest. This is performed by identifying new or existing NLP tasks which can address reader motivation, designing novel machine learning approaches to perform these tasks, and evaluating and examining these approaches to determine what they can teach us about the factors of reader motivation. In executing this research, we make use of concepts from NLP such as textual coherence, interestingness, and summarization. We additionally use techniques from ML including supervised and self-supervised learning, deep neural networks, and sentence embeddings. This thesis, presented in an integrated-article format, contains three core contributions among its three articles. In the first article, we propose a flexible and insightful approach to coherence estimation. This approach uses a new sentence embedding which reflects predicted position distributions. Second, we introduce the new task of pull quote selection, examining a spectrum of approaches in depth. This article identifies several concrete heuristics for finding interesting sentences, both expected and unexpected. Third, we introduce a new interactive summarization task called HARE (Hone as You Read), which is especially suitable for mobile devices. Quantitative and qualitative analysis support the practicality and potential usefulness of this new type of summarization.","abstract_has_math":false,"creators":["Bohn, Tanner A"],"institution":"The University of Western Ontario","degree_name":"Ph D","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":[],"advisors":["Ling, Charles X."],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-04-29","date_published":"2021-04-29","updated_at":"2026-07-27T21:56:20Z","subjects":["natural language processing","machine learning","internal motivation","reading","coherence","sentence embedding","attention","situational interest","pull quotes","interactive summarization"],"languages":["en_ca"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/20.500.14721/31025","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Ling, Charles X."]},{"key":"dc:creator","label":"Author","values":["Bohn, Tanner A"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-07-10T18:49:50Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-10T18:49:50Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-04-29"]},{"key":"dc:publisher","label":"Institution","values":["The University of Western Ontario"]},{"key":"dc:type","label":"Dc Type","values":["thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph D"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["natural language processing","machine learning","internal motivation","reading","coherence","sentence embedding","attention","situational interest","pull quotes","interactive summarization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_ca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/20.500.14721/31025"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Collaborative Specialization: Artificial Intelligence","The thesis cover page in the PDF document includes references to Western University’s previous institutional repository platform, known as Scholarship@Western, and links to that platform (beginning with ir.lib.uwo.ca). In citing or referring to this thesis, use the DOI or handle from this page instead. Sample citation: Author name, \"Thesis title.\" (Year). Western University Open Repository. https://doi.org/10.71858/123456."]},{"key":"dc:description.abstract","label":"Abstract","values":["This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success. There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the best of our knowledge, this is the first work to explicitly address the broad and meaningful impact that NLP and ML can have on the reading experience. In particular, the aim of this thesis is to explore new approaches to supporting internal reading motivation, which is influenced by readability, situational interest, and personal interest. This is performed by identifying new or existing NLP tasks which can address reader motivation, designing novel machine learning approaches to perform these tasks, and evaluating and examining these approaches to determine what they can teach us about the factors of reader motivation. In executing this research, we make use of concepts from NLP such as textual coherence, interestingness, and summarization. We additionally use techniques from ML including supervised and self-supervised learning, deep neural networks, and sentence embeddings. This thesis, presented in an integrated-article format, contains three core contributions among its three articles. In the first article, we propose a flexible and insightful approach to coherence estimation. This approach uses a new sentence embedding which reflects predicted position distributions. Second, we introduce the new task of pull quote selection, examining a spectrum of approaches in depth. This article identifies several concrete heuristics for finding interesting sentences, both expected and unexpected. Third, we introduce a new interactive summarization task called HARE (Hone as You Read), which is especially suitable for mobile devices. 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Western University Open Repository. https://doi.org/10.71858/123456."],"dc:description.abstract":["This thesis focuses on the problem of increasing reading motivation with machine learning (ML). The act of reading is central to modern human life, and there is much to be gained by improving the reading experience. For example, the internal reading motivation of students, especially their interest and enjoyment in reading, are important factors in their academic success. There are many topics in natural language processing (NLP) which can be applied to improving the reading experience in terms of readability, comprehension, reading speed, motivation, etc. Such topics include personalized recommendation, headline optimization, text simplification, and many others. However, to the best of our knowledge, this is the first work to explicitly address the broad and meaningful impact that NLP and ML can have on the reading experience. In particular, the aim of this thesis is to explore new approaches to supporting internal reading motivation, which is influenced by readability, situational interest, and personal interest. This is performed by identifying new or existing NLP tasks which can address reader motivation, designing novel machine learning approaches to perform these tasks, and evaluating and examining these approaches to determine what they can teach us about the factors of reader motivation. In executing this research, we make use of concepts from NLP such as textual coherence, interestingness, and summarization. We additionally use techniques from ML including supervised and self-supervised learning, deep neural networks, and sentence embeddings. This thesis, presented in an integrated-article format, contains three core contributions among its three articles. In the first article, we propose a flexible and insightful approach to coherence estimation. This approach uses a new sentence embedding which reflects predicted position distributions. Second, we introduce the new task of pull quote selection, examining a spectrum of approaches in depth. This article identifies several concrete heuristics for finding interesting sentences, both expected and unexpected. Third, we introduce a new interactive summarization task called HARE (Hone as You Read), which is especially suitable for mobile devices. Quantitative and qualitative analysis support the practicality and potential usefulness of this new type of summarization."],"dc:identifier.uri":["https://hdl.handle.net/20.500.14721/31025"],"dc:language.iso":["en_ca"],"dc:publisher":["The University of Western Ontario"],"dc:subject":["natural language processing","machine learning","internal motivation","reading","coherence","sentence embedding","attention","situational interest","pull quotes","interactive summarization"],"dc:title":["Improving Reader Motivation with Machine Learning"],"dc:type":["thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Ph D"]},"updated_at":"2026-07-27T21:56:20Z"}