Mining the Gaps: Towards Polynomial Summarization

Marina Litvak, Natalia Vanetik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

The problem of text summarization for a collection of documents is defined as the problem of selecting a small subset of sentences so that the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a linear model for the problem of text summarization1, where a summary preserves the information coverage as much as possible in comparison to the original document set. We reduce the problem of finding the best summary to the problem of finding the point on a convex polytope closest to the given hyperplane, and solve it efficiently with the help of fractional (polynomial-time) linear programming. The experimental results show the superiority of our approach over most of the systems participating in the generic multi-document summarization task (MultiLing) of the TAC 2011 competition.

Original languageEnglish
Title of host publication6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference
EditorsRuslan Mitkov, Jong C. Park
PublisherAsian Federation of Natural Language Processing
Pages655-660
Number of pages6
ISBN (Electronic)9784990734800
StatePublished - 1 Jan 2013
Externally publishedYes
Event6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Nagoya, Japan
Duration: 14 Oct 2013 → …

Publication series

Name6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference

Conference

Conference6th International Joint Conference on Natural Language Processing, IJCNLP 2013
Country/TerritoryJapan
CityNagoya
Period14/10/13 → …

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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