Improving Reader Motivation with Machine Learning

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.

Name thesis:degree_name

Ph D

Discipline thesis:degree_discipline

Computer Science

Grantor dc:publisher

The University of Western Ontario

Year dc:date.issued

2021

Author dc:creator

• Bohn, Tanner A

Advisor dc:contributor.advisor

• Ling, Charles X.

• natural language processing
• machine learning
• internal motivation
• reading
• coherence
• sentence embedding
• attention
• situational interest
• pull quotes
• interactive summarization

Language dc:language.iso

en_ca

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14721/31025

OAI identifier oai:identifier

oai:uwo.scholaris.ca:20.500.14721/31025