# Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly

## TL;DR

This paper introduces a novel streaming algorithm for non-monotone submodular maximization, enabling real-time, personalized video summarization with high efficiency and comparable quality to existing methods.

## Contribution

It presents the first efficient single-pass streaming algorithm for non-monotone submodular maximization with provable approximation guarantees under complex constraints.

## Key findings

- Runs over 1700 times faster than previous methods
- Maintains similar summarization quality
- Effective for real-time personalized video summarization

## Abstract

The need for real time analysis of rapidly producing data streams (e.g., video and image streams) motivated the design of streaming algorithms that can efficiently extract and summarize useful information from massive data "on the fly". Such problems can often be reduced to maximizing a submodular set function subject to various constraints. While efficient streaming methods have been recently developed for monotone submodular maximization, in a wide range of applications, such as video summarization, the underlying utility function is non-monotone, and there are often various constraints imposed on the optimization problem to consider privacy or personalization. We develop the first efficient single pass streaming algorithm, Streaming Local Search, that for any streaming monotone submodular maximization algorithm with approximation guarantee $\alpha$ under a collection of independence systems ${\cal I}$, provides a constant $1/\big(1+2/\sqrt{\alpha}+1/\alpha +2d(1+\sqrt{\alpha})\big)$ approximation guarantee for maximizing a non-monotone submodular function under the intersection of ${\cal I}$ and $d$ knapsack constraints. Our experiments show that for video summarization, our method runs more than 1700 times faster than previous work, while maintaining practically the same performance.