LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

LVSum addresses a critical gap in multimodal evaluation: the lack of high-quality, human-verified ground truth for long-context video with specific timestamp requirements. While many benchmarks focus on short clips (e.g., MSR-VTT) or general QA (e.g., Video-MME), LVSum targets the 'summarization' and 'temporal grounding' aspect which is a high-priority frontier for labs like Google (Gemini 1.5 Pro) and OpenAI (Sora/GPT-4o). The defensibility is currently a 4 because, while human-annotated data is expensive and provides a minor moat, the project is brand new (6 days old) with zero stars, indicating it has not yet achieved 'standard' status. Its value depends entirely on research community adoption; if researchers don't cite it or use it for leaderboard rankings, it will be superseded by lab-internal benchmarks or more popular alternatives like Video-MME. The 4 forks suggest very early-stage interest or internal lab activity. Platform domination risk is low because benchmarks are generally seen as neutral ground, though frontier labs may effectively 'solve' the benchmark quickly given the rapid progress in long-context window processing. The primary risk is displacement by a larger, more comprehensive dataset (e.g., one containing 10k+ videos instead of a limited 13-domain sample) or the industry moving toward automated 'LLM-as-a-judge' evaluation that renders static human benchmarks less relevant.