告诉我需要知道什么：探索基于 LLM 的（个性化）抽象式多源会议摘要
Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization
AI总结：
提出三阶段 LLM 多源会议摘要框架，融合补充材料并按参与者特征个性化，显著提升摘要相关性与信息量，同时评估多模型性能—成本权衡。
摘要：
会议摘要在数字通信中至关重要，但现有解决方案在两方面存在困难：一是显著性识别，以生成个性化、可执行的摘要；二是上下文理解，以充分领会会议内容。此前通过在转录文本之外考虑相关补充资源（例如演示幻灯片）来解决这些问题的尝试，受到模型有限上下文规模以及处理多源任务额外复杂性的阻碍，例如识别附加文件中的相关信息，并将其与会议内容无缝对齐。本工作通过一种三阶段大语言模型方法，探索考虑补充材料的多源会议摘要：识别需要额外上下文的转录文本段落，从补充材料中推断相关细节并将其插入转录文本，然后基于这一增强后的转录文本生成摘要。我们的多源方法增强了模型理解，使摘要相关性提高约 9%，并产出内容更丰富的输出。我们引入了一种个性化协议，用于提取参与者特征并相应定制摘要，使信息量提高约 10%。本工作还进一步给出了跨四个主流模型系列的性能—成本权衡洞见，包括可在边缘设备上运行的选项。我们的方法可扩展到类似的、可从额外资源和个性化中受益的复杂生成任务，例如对话系统和行动计划。
英文摘要：
Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context understanding to fully comprehend the meetings' content. Previous attempts to address these issues by considering related supplementary resources (e.g., presentation slides) alongside transcripts are hindered by models' limited context sizes and handling the additional complexities of the multi-source tasks, such as identifying relevant information in additional files and seamlessly aligning it with the meeting content. This work explores multi-source meeting summarization considering supplementary materials through a three-stage large language model approach: identifying transcript passages needing additional context, inferring relevant details from supplementary materials and inserting them into the transcript, and generating a summary from this enriched transcript. Our multi-source approach enhances model understanding, increasing summary relevance by ~9% and producing more content-rich outputs. We introduce a personalization protocol that extracts participant characteristics and tailors summaries accordingly, improving informativeness by ~10%. This work further provides insights on performance-cost trade-offs across four leading model families, including edge-device capable options. Our approach can be extended to similar complex generative tasks benefitting from additional resources and personalization, such as dialogue systems and action planning.
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。
