Abstract

Abstract. In this work we study the theoretical and empirical properties of various global inference algorithms for multi-document summarization. We start by defining a general framework and proving that inference in it is NP-hard. We then present three algorithms: The first is a greedy approximate method, the second a dynamic programming approach based on solutions to the knapsack problem, and the third is an exact algorithm that uses an Integer Linear Programming formulation of the problem. We empirically evaluate all three algorithms and show that, relative to the exact solution, the dynamic programming algorithm provides near optimal results with preferable scaling properties. 1

Citations