Video Large Language Models (VLLMs) have recently demonstrated strong capabilities in multimodal understanding. Yet, their effectiveness in structured video-to-text summarization remains insufficiently studied, as most evaluations focus solely on plain-text outputs. This thesis investigates the zero-shot capabilities of six state-of-the-art VLLMs across five distinct summary formats: plain text, event-based, spatially contextualized, timeline, and spatio-temporal. The analysis reveals persistent limitations in structured summaries, particularly in providing valid temporal annotations and adhering to required output formats. To address these challenges, four categories of mitigation strategies are introduced. The first employs Chain-of-Thought prompting, where external video knowledge extracted from lightweight models is injected into prompts to guide temporal and spatial reasoning.