Data Summarization at Scale: A Two-Stage Submodular Approach
This addresses the need for scalable summarization techniques in large datasets, offering incremental improvements with streaming and distributed solutions.
The paper tackles the problem of data summarization at scale by introducing a two-stage submodular framework to efficiently reduce large datasets while maintaining near-optimal performance for new functions, demonstrating utility and efficiency in real-world tasks like image summarization and ride-share optimization.
The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal solutions in linear time. We focus on a two-stage submodular framework where the goal is to use some given training functions to reduce the ground set so that optimizing new functions (drawn from the same distribution) over the reduced set provides almost as much value as optimizing them over the entire ground set. In this paper, we develop the first streaming and distributed solutions to this problem. In addition to providing strong theoretical guarantees, we demonstrate both the utility and efficiency of our algorithms on real-world tasks including image summarization and ride-share optimization.