A SVM-based ensemble approach to multi-document summarization

Yllias Chali, Sadid A. Hasan, Shafiq Rayhan Joty

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

14 Citations (Scopus)

Abstract

In this paper, we present a Support Vector Machine (SVM) based ensemble approach to combat the extractive multi-document summarization problem. Although SVM can have a good generalization ability, it may experience a performance degradation through wrong classifications. We use a committee of several SVMs, i.e. Cross-Validation Committees (CVC), to form an ensemble of classifiers where the strategy is to improve the performance by correcting errors of one classifier using the accurate output of others. The practicality and effectiveness of this technique is demonstrated using the experimental results.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages199-202
Number of pages4
Volume5549 LNAI
DOIs
Publication statusPublished - 10 Sep 2009
Externally publishedYes
Event22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009 - Kelowna, BC, Canada
Duration: 25 May 200927 May 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5549 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other22nd Canadian Conference on Artificial Intelligence, Canadian AI 2009
CountryCanada
CityKelowna, BC
Period25/5/0927/5/09

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Keywords

  • Cross-Validation Committees
  • Ensemble
  • Multi-Document Summarization
  • Support Vector Machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chali, Y., Hasan, S. A., & Rayhan Joty, S. (2009). A SVM-based ensemble approach to multi-document summarization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5549 LNAI, pp. 199-202). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5549 LNAI). https://doi.org/10.1007/978-3-642-01818-3_23