TY - GEN
T1 - Text summarization while maximizing multiple objectives with Lagrangian relaxation
AU - Nishino, Masaaki
AU - Yasuda, Norihito
AU - Hirao, Tsutomu
AU - Suzuki, Jun
AU - Nagata, Masaaki
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84875437961&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875437961&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-36973-5_81
DO - 10.1007/978-3-642-36973-5_81
M3 - Conference contribution
AN - SCOPUS:84875437961
SN - 9783642369728
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 772
EP - 775
BT - Advances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings
T2 - 35th European Conference on Information Retrieval, ECIR 2013
Y2 - 24 March 2013 through 27 March 2013
ER -