In this paper, we introduce Spotlight, a novel paradigm for information extraction that produces concise, engaging narratives by highlighting the most compelling aspects of a document. Unlike traditional summaries, which prioritize comprehensive coverage, spotlights selectively emphasize intriguing content to foster deeper reader engagement with the source material. We formally differentiate spotlights from related constructs and support our analysis with a detailed benchmarking study using new datasets curated for this work. To generate high-quality spotlights, we propose a two-stage approach: fine-tuning a large language model on our benchmark data, followed by alignment via Direct Preference Optimization (DPO). Our comprehensive evaluation demonstrates that the resulting model not only identifies key elements with precision but also enhances readability and boosts the engagement value of the original document.

翻译：本文介绍了一种新颖的信息提取范式——Spotlight，它通过突出文档中最具吸引力的方面，生成简洁而引人入胜的叙述。与传统摘要优先追求全面覆盖不同，Spotlight有选择性地强调有趣内容，以促进读者对源材料的深度参与。我们正式区分了Spotlight与相关概念，并通过使用为本研究专门构建的新数据集进行的详细基准测试来支持我们的分析。为了生成高质量的Spotlight，我们提出了一种两阶段方法：首先在我们的基准数据上微调一个大语言模型，然后通过直接偏好优化（DPO）进行对齐。我们的综合评估表明，所得模型不仅能精确识别关键要素，还能提升可读性并增强原文的参与价值。