Semantic matching improves search and summaries for coding videos
Intelligent Semantic Matching (ISM) for Video Tutorial Search using Transformer Models
Software Engineering
Summary
Finding the right software tutorial videos can be hard because usual searches only look for keywords, missing the true meaning behind questions. The authors created ISM, which understands the meaning of video transcripts to better match search queries. It breaks videos into parts and reorders results to keep the context clear. ISM also uses AI to make helpful video summaries so developers can quickly decide if a video is useful. Their tests showed ISM finds better matches and users liked the smart search and summaries.
What this means in practice
- •For software developers: Find programming tutorial videos more relevant to complex queries using semantic understanding of content and AI-generated summaries.
- •For video platform engineers: Enhance video search systems by integrating semantic vector matching and automated summary generation to improve user experience.
Authors
Ahmad J. Tayeb, Sonia Haiduc
Abstract
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.