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 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.
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McDonald, R. (2007). A Study of Global Inference Algorithms in Multi-document Summarization. In: Amati, G., Carpineto, C., Romano, G. (eds) Advances in Information Retrieval. ECIR 2007. Lecture Notes in Computer Science, vol 4425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71496-5_51
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DOI: https://doi.org/10.1007/978-3-540-71496-5_51
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