Imagine sitting in a presentation, trying to follow the speaker while simultaneously scanning the slides for relevant information. While the entire slide is visible, identifying the relevant regions can be challenging. As you focus on one part of the slide, the speaker moves on to a new sentence, leaving you scrambling to catch up visually. This constant back-and-forth creates a disconnect between what is being said and the most important visual elements, making it hard to absorb key details, especially in fast-paced or content-heavy presentations such as conference talks. This requires an understanding of slides, including text, graphics, and layout. We introduce a method that automatically identifies and highlights the most relevant slide regions based on the speaker's narrative. By analyzing spoken content and matching it with textual or graphical elements in the slides, our approach ensures better synchronization between what listeners hear and what they need to attend to. We explore different ways of solving this problem and assess their success and failure cases. Analyzing multimedia documents is emerging as a key requirement for seamless understanding of content-rich videos, such as educational videos and conference talks, by reducing cognitive strain and improving comprehension. Code and dataset are available at: https://github.com/meghamariamkm2002/Slide_Highlight

翻译：想象一下，您正坐在一场演示中，一边试图跟上演讲者的节奏，一边同时扫描幻灯片以寻找相关信息。虽然整个幻灯片都清晰可见，但识别出相关区域却可能颇具挑战性。当您专注于幻灯片的某一部分时，演讲者可能已经转向了新的句子，迫使您手忙脚乱地追赶视觉信息。这种持续的来回切换造成了所听内容与最重要视觉元素之间的脱节，使得听众难以吸收关键细节，尤其是在快节奏或内容密集的演示中，例如会议报告。这需要理解幻灯片的内容，包括文本、图形和布局。我们提出了一种方法，能够根据演讲者的叙述自动识别并突出显示幻灯片中最相关的区域。通过分析口语内容并将其与幻灯片中的文本或图形元素进行匹配，我们的方法确保了听众听到的内容与他们需要关注的内容之间更好的同步。我们探讨了解决此问题的不同方法，并评估了它们的成功与失败案例。通过减少认知负荷并提高理解力，分析多媒体文档正逐渐成为无缝理解内容丰富视频（如教育视频和会议报告）的关键需求。代码和数据集可在以下网址获取：https://github.com/meghamariamkm2002/Slide_Highlight