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 for inference in summarization. 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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
McDonald, R. (2007). A study of global inference algorithms in multi-document summarization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4425 LNCS, pp. 557–564). Springer Verlag. https://doi.org/10.1007/978-3-540-71496-5_51
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