Abstract
Automated semantic annotation of broadcast television content presents distinctive challenges, combining structured audiovisual composition, domain-specific editorial patterns, and strict operational constraints. While multimodal large language models (MLLMs) have demonstrated strong general-purpose video understanding capabilities, their comparative effectiveness across pipeline architectures and input configurations in broadcast-specific settings remains empirically undercharacterized. This paper presents a systematic evaluation of multimodal annotation pipelines applied to broadcast television news in the Italian setting. We construct a domain-specific benchmark of clips labeled across four semantic dimensions: visual environment classification, topic classification, sensitive content detection, and named entity recognition. Two different pipeline architectures are evaluated across nine frontier models, including Gemini 3.0 Pro, LLaMA 4 Maverick, Qwen-VL variants, and Gemma 3, under progressively enriched input strategies combining visual signals, automatic speech recognition, speaker diarization, and metadata. Experimental results demonstrate that gains from video input are strongly model-dependent: larger models effectively leverage temporal continuity, while smaller models show performance degradation under extended multimodal context, likely due to token overload. Beyond benchmarking, the selected pipeline is deployed on 14 full broadcast episodes, with minute-level annotations integrated with normalized audience measurement data provided by an Italian media company. This integration enables correlational analysis of topic-level audience sensitivity and generational engagement divergence, demonstrating the operational viability of the proposed framework for content-based audience analytics.
Abstract (translated)
广播电视内容的自动化语义标注面临独特挑战，需综合处理结构化的视听构成、领域特定的编辑模式以及严格的操作限制。尽管多模态大语言模型（MLLMs）已展现出强大的通用视频理解能力，但针对广播电视特定场景下不同流水线架构与输入配置的相对有效性，尚缺乏实证研究。本文系统评估了应用于意大利语境下电视新闻的多模态标注流水线。我们构建了涵盖四个语义维度的领域专用基准数据集：视觉环境分类、话题分类、敏感内容检测与命名实体识别。实验在九种前沿模型（包括Gemini 3.0 Pro、LLaMA 4 Maverick、Qwen-VL系列及Gemma 3）上对比了两种流水线架构，并采用渐进式增强的输入策略，逐步融合视觉信号、自动语音识别、说话人分离与元数据。实验结果表明，视频输入带来的性能提升高度依赖模型规模：大模型能有效利用时间连续性，而小模型在扩展多模态上下文时出现性能退化，可能源于token过载。除基准测试外，选定流水线已部署于14集完整广播节目，其分钟级标注与意大利媒体公司提供的标准化收视率数据实现整合。该整合支持了话题级受众敏感度与代际参与差异的相关性分析，验证了所提框架在基于内容的受众分析中的 operational viability。
URL
https://arxiv.org/abs/2603.26772
