Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents

Analyzing and processing vast amounts of textual data presents significant challenges in efficiently extracting key information. 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 highlights (fragmented key points) and 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 twostage 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. Datasets and code are available at https://github.com/ ankan2/Spotlight-EMNLP2025 .