Abstract
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.
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Nishino, M., Yasuda, N., Hirao, T., Suzuki, J., Nagata, M. (2013). Text Summarization while Maximizing Multiple Objectives with Lagrangian Relaxation. In: Serdyukov, P., et al. Advances in Information Retrieval. ECIR 2013. Lecture Notes in Computer Science, vol 7814. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36973-5_81
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DOI: https://doi.org/10.1007/978-3-642-36973-5_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36972-8
Online ISBN: 978-3-642-36973-5
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