LVSum：一个用于时间感知长视频摘要的基准测试
LVSum: A Benchmark for Timestamp-Aware Long Video Summarization
AI总结：
本文提出LVSum基准测试，用于评估长视频摘要中时间对齐的性能，通过引入新的评估指标揭示现有MLLM在时间理解上的系统性差距。
摘要：
长视频摘要对当前多模态大语言模型（MLLMs）提出了重大挑战，特别是在长时间段内保持时间准确性以及生成同时具有语义和时间基础的摘要方面。在本工作中，我们提出了LVSum，一个由人类标注的基准测试，专门用于评估具有精细时间对齐的长视频摘要。LVSum包含来自13个不同领域的多样化长视频，每部视频都配有由人类生成的包含精确时间参考的摘要。我们对专有和开源的MLLMs在LVSum上进行了全面评估，使用新引入的基于LLM的指标评估内容相关性和模态一致性，同时结合标准评估指标。我们的实验揭示了现有MLLM在时间理解上的系统性差距，并提供了见解，为推进长视频摘要中的时间推理奠定了新基础。
英文摘要：
Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded. We introduce LVSum, a human-annotated benchmark for evaluating long-form video summarization with fine-grained temporal alignment. LVSum comprises 72 diverse videos spanning 13 domains with an average duration of 16 minutes, each annotated with up to 10 human-generated summaries containing temporal references. We conduct a comprehensive evaluation of leading proprietary and open-source MLLMs using newly introduced LLM-based metrics for content relevance and modality coherence, alongside standard automatic metrics. Our experiments reveal three key findings: (1) transcripts contribute substantially more to summarization quality than visual frames alone, (2) a significant performance gap persists between model-generated and human-written summaries, and (3) current MLLMs exhibit systematic weaknesses in temporal grounding, instruction adherence, and cross-modal coherence. We release the dataset and code.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
