We show an extractive text summarization method that solves an optimization problem involving the maximization of multiple objectives. Though we can obtain high quality summaries if we solve the problem exactly with our formulation, it is NP-hard and cannot scale to support large problem size. Our solution is an efficient and high quality approximation method based on Lagrangian relaxation (LR) techniques. In experiments on the DUC'04 dataset, our LR based method matches the performance of state-of-the-art methods. © 2013 Springer-Verlag.
CITATION STYLE
Nishino, M., Yasuda, N., Hirao, T., Suzuki, J., & Nagata, M. (2013). Text summarization while maximizing multiple objectives with Lagrangian relaxation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7814 LNCS, pp. 772–775). https://doi.org/10.1007/978-3-642-36973-5_81
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