Before we dive into the solution, let's look at the specific challenges that come with video summarization:
- Information overload: The sheer volume of available videos makes locating specific content or topics of interest increasingly difficult.
- Subjectivity in summarization: Conventional approaches to video summarization frequently grapple with subjectivity, resulting in inconsistent or inadequate summaries.
- Lengthy videos: Long-form videos pose a significant barrier to efficient information retrieval, requiring users to sift through extensive footage to find relevant segments.
- Content relevance: Automatically shortening videos without sacrificing content relevance and key messages presents a considerable technical hurdle.
We kept these challenges in mind and aimed to create genAI-based Video Summarization and Smart Search, a solution powered by Language Learning Models (LLMs) and bolstered by our expertise in cloud technologies. Our innovative approach not only streamlines the process of video summarization but also revolutionizes how users interact with video content. By harnessing the capabilities of advanced AI technology and leveraging Virtusa's cloud capabilities, we offer a comprehensive suite of features designed to enhance your video browsing experience.
Virtusa's genAI-based Video Summarization and Smart Search solution addresses these challenges head-on with a comprehensive set of features designed to simplify the video consumption and search process:
LLM-powered summarization
Our solution leverages the power of LLMs to generate concise and informative summaries of videos, eliminating the need to watch lengthy content in its entirety.
Smart query interface
Our intelligent query interface allows users to pose questions based on video content, seamlessly redirecting them to the precise timestamp within the video that addresses their query.
Automatic topic-based video generation
Our solution automatically generates short videos organized by key topics, allowing users to quickly access relevant information without navigating through lengthy footage.
Transcript extraction with timestamps
Our system automatically extracts and associates video transcripts with timestamps, facilitating efficient content retrieval based on user queries.
Vector similarity-based chunk retrieval
We retrieve relevant video chunks based on user queries through advanced vector similarity algorithms, ensuring accurate and timely information retrieval.
LLM-powered stitching
Our solution utilizes LLMs to stitch together retrieved video chunks seamlessly, providing users with cohesive and informative answers to their queries.