Text summarization while maximizing multiple objectives with Lagrangian relaxation

Masaaki Nishino, Norihito Yasuda, Tsutomu Hirao, Jun Suzuki, Masaaki Nagata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationAdvances in Information Retrieval - 35th European Conference on IR Research, ECIR 2013, Proceedings
Pages772-775
Number of pages4
DOIs
Publication statusPublished - 2013 Apr 2
Externally publishedYes
Event35th European Conference on Information Retrieval, ECIR 2013 - Moscow, Russian Federation
Duration: 2013 Mar 242013 Mar 27

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7814 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other35th European Conference on Information Retrieval, ECIR 2013
Country/TerritoryRussian Federation
CityMoscow
Period13/3/2413/3/27

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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