Generating academic paper titles requires balancing two key goals: faithfulness to the content and attractiveness to readers. Existing methods often treat title generation as a simple summarization task, producing accurate but unengaging titles. While multi-objective Direct Preference Optimization (DPO) offers a potential solution, our empirical analysis shows that combining conflicting objectives into a single reward is associated with substantial gradient interference during training. To address this problem, drawing inspiration from Pareto dominance in multi-objective optimization, we propose Multi-Objective Dominance Alignment (MODA), a data-centric training strategy tailored for academic title generation. MODA retains preference pairs with strict Pareto dominance, thereby reducing exposure to supervision with opposing reward directions. We implement MODA using dual reward models to independently score both dimensions. Across our experiments, this filtering strategy improves the observed balance between faithfulness and attractiveness, outperforms Human Author baselines, and approaches the performance of frontier models despite using an 8B backbone. Our code is publicly available at https://github.com/realyoume/title_it_right.