Advertisement
Published in:
2013 | OriginalPaper | Chapter
Text Summarization while Maximizing Multiple Objectives with Lagrangian Relaxation
Authors : Masaaki Nishino, Norihito Yasuda, Tsutomu Hirao, Jun Suzuki, Masaaki Nagata
Published in: Advances in Information Retrieval
Publisher: Springer Berlin Heidelberg
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.