📝 SummarXiv

Abstract

Graph-based ranking methods, such as LexRank, are fundamental in Natural Language Processing (NLP) applications like text summarization, as they measure the relative importance of textual units. Building on recent advances in ranking methods for growing and dynamic graphs, we develop a robust variant of LexRank that operates on stochastic similarity graphs with uncertain and expanding structure. Our approach introduces a novel $l_1$-based formulation that captures ambiguity in both transition probabilities and graph size, while maintaining sparsity. The resulting non-convex problem is upper-bounded by a linear program, providing a tractable and interpretable approximation.