Assessing and Enhancing the Quality of News Headlines Using Machine Learning
Bibliographic record
Abstract
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 We then use this labeled data to train our novel headline quality prediction model to predict the quality of unpublished headlines, assisting journalists in selecting high-quality headlines for their articles.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".