Text summarization while maximizing multiple objectives with lagrangian relaxation

Authors:
Masaaki Nishino;Norihito Yasuda;Tsutomu Hirao;Jun Suzuki;Masaaki Nagata
Affiliations:
NTT Communication Science Laboratories, NTT Corporation, Japan;NTT Communication Science Laboratories, NTT Corporation, Japan;NTT Communication Science Laboratories, NTT Corporation, Japan;NTT Communication Science Laboratories, NTT Corporation, Japan;NTT Communication Science Laboratories, NTT Corporation, Japan
Venue:
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Year:
2013

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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.