Stream Clipper: Scalable Submodular Maximization on Stream

TL;DR

Stream Clipper is a scalable streaming algorithm for submodular maximization that achieves near-offline performance in document/video summarization by adaptively managing elements with thresholds and buffer, balancing efficiency and quality.

Contribution

It introduces an adaptive threshold streaming algorithm that matches offline greedy performance under certain conditions, with improved efficiency for summarization tasks.

Findings

Outperforms other streaming methods in summarization quality

Uses less computation and memory than offline algorithms

Achieves approximation factors close to the theoretical bound in practice

Abstract

We propose a streaming submodular maximization algorithm "stream clipper" that performs as well as the offline greedy algorithm on document/video summarization in practice. It adds elements from a stream either to a solution set $S$ or to an extra buffer $B$ based on two adaptive thresholds, and improves $S$ by a final greedy step that starts from $S$ adding elements from $B$. During this process, swapping elements out of $S$ can occur if doing so yields improvements. The thresholds adapt based on if current memory utilization exceeds a budget, e.g., it increases the lower threshold, and removes from the buffer $B$ elements below the new lower threshold. We show that, while our approximation factor in the worst case is $1/2$ (like in previous work, and corresponding to the tight bound), we show that there are data-dependent conditions where our bound falls within the range $[1/2, 1-1/e]$. In news and video summarization experiments, the algorithm consistently outperforms other streaming methods, and, while using significantly less computation and memory, performs similarly to the offline greedy algorithm.