智能语义匹配（ISM）：基于Transformer模型的视频教程搜索
Intelligent Semantic Matching (ISM) for Video Tutorial Search using Transformer Models
- King Abdulaziz University（阿卜杜勒阿齐兹国王大学）
- Florida State University（佛罗里达州立大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
针对现有视频教程搜索方法无法捕捉语义和用户意图的问题，提出ISM方法，利用SBERT生成语义向量并重排序，结合GPT-4生成摘要，在检索和片段识别上优于基线，获用户偏好。
中文摘要 AI 辅助
软件开发视频教程的数量和多样性不断增加，增强了开发者的数字学习体验，但也带来了高效定位相关内容的挑战。现有的视频搜索方法，包括基于关键词的方法以及CodeTube和TechTube等工具，主要依赖BM25等检索算法，这些算法无法捕捉搜索查询背后的语义细微差别和用户意图。为解决这些局限性，我们引入了ISM方法，该方法使用SBERT从视频教程转录文本中生成语义丰富的向量，以改进编程视频教程的搜索。通过分段转录文本并实施重排序过程，ISM有效保留了上下文并增强了搜索结果的相关性。此外，ISM使用GPT-4生成信息丰富的视频摘要，使开发者能够快速评估视频内容的相关性。为评估我们的方法，我们首先进行了一项定量研究，将ISM与基线TechTube进行比较。结果显示，ISM在视频检索和片段识别方面均表现更优，Hit@5得分为0.95，平均F1得分为0.70，而基线分别为0.58和0.52。我们还进行了一项用户研究，结果显示用户强烈偏好我们方法的语义匹配能力和AI生成的摘要。这项工作通过提供更细致且更符合用户需求的检索和摘要机制，推进了编程视频教程搜索和摘要领域的最新水平。
英文摘要
The rise in the number and diversity of available software development video tutorials has enhanced digital learning for developers but also introduced challenges in locating relevant content efficiently. Existing video search methods, including keyword-based approaches and tools like CodeTube and TechTube, rely primarily on retrieval algorithms such as BM25, which fail to capture the semantic nuances and user intentions behind search queries. To address these limitations, we introduce ISM, an approach that uses SBERT to generate semantically rich vectors from video tutorial transcripts to improve the search for programming video tutorials. By segmenting transcripts and implementing a re-ranking process, ISM effectively preserves context and enhances the relevance of search results. Additionally, ISM generates informative video summaries using GPT-4, allowing developers to quickly assess the relevance of video content. To evaluate our approach, we first performed a quantitative study comparing ISM with the baseline TechTube. The results revealed that ISM performs better in both video retrieval and fragment identification, achieving a Hit@5 score of 0.95 and an average F1 score of 0.70 compared to the baseline's 0.58 and 0.52, respectively. We also performed a user study, which revealed that users strongly preferred the semantic matching capabilities and AI-generated summaries of our approach. This work advances the state-of-the-art in programming video tutorial search and summarization by offering more nuanced and user-aligned retrieval and summarization mechanisms.
