Assessing And Enhancing The Quality Of News Headlines Using Machine Learning

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dc.contributor.advisorAijun An
dc.contributor.authorOmidvar, Amin
dc.date.accessioned2025-07-23T15:09:33Z
dc.date.available2025-07-23T15:09:33Z
dc.date.copyright2025-03-04
dc.date.issued2025-07-23
dc.date.updated2025-07-23T15:09:33Z
dc.degree.disciplineComputer Science
dc.degree.levelDoctoral
dc.degree.namePhD - Doctor of Philosophy
dc.description.abstractHeadlines play a pivotal role in capturing readers' attention, and their quality is critical for engaging audiences. In this thesis, we propose various solutions to assist news media in crafting high-quality headlines. First, we delve into headline quality assessment, devising four innovative indicators that automatically evaluate headlines' quality. Our proposed model empowers news outlets to automatically determine the quality of published headlines. We evaluate the quality of headlines from The Globe and Mail using these four indicators and provide insightful results. We then use this labeled data to train our novel headline quality prediction model to predict the quality of unpublished headlines, assisting journalists in selecting high-quality headlines for their articles. Furthermore, we facilitate journalists' work by recommending high-quality headlines for their articles. To accomplish this, we propose a headline generative model that learns to generate headlines using Reinforcement Learning (RL). Our model can be optimized not only with respect to a non-differentiable metric but also based on a combination of two different metrics simultaneously. Additionally, we enhance headline generation in terms of both training speed and the quality of the generated headlines by proposing a novel architecture utilizing state-of-the-art transformer models. In our architecture, after generating candidate headlines using state-of-the-art models, we select the most popular headline using our headline popularity prediction model. Moreover, we establish a popularity benchmark for evaluating headline generation models based on their ability to generate popular headlines. Lastly, we forecast changes in how people consume news articles, envisioning a shift towards interacting with agents instead of navigating news portals. To address existing challenges and enable this transition, we introduce Semantic In-Context Learning (S-ICL), an innovative approach enabling Large Language Models (LLMs) to deliver updated news in a conversational format, enhancing user engagement and comprehension for news media.
dc.identifier.urihttps://hdl.handle.net/10315/42957
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsHeadline Quality Assessment
dc.subject.keywordsHeadline Generation
dc.subject.keywordsNatural Language Processing
dc.subject.keywordsConversational AI for News
dc.subject.keywordsTransformer Models
dc.subject.keywordsMachine Learning
dc.subject.keywordsDeep Learning
dc.titleAssessing And Enhancing The Quality Of News Headlines Using Machine Learning
dc.typeElectronic Thesis or Dissertation

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