Assessing And Enhancing The Quality Of News Headlines Using Machine Learning

Headlines 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 We then use this labeled data to train our novel headline quality prediction model to predict the quality of unpublished headlines, assisting journalists in selecting high-quality headlines for their articles.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.