An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts

Muhidin Mohamed, Mourad Oussalah

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

Abstract

This paper proposes an innovative graph-based text summarization model for generic single and multi-document summarization. The approach involves four unique processing stages: parsing sentences semantically using Semantic Role Labeling (SRL), grouping semantic arguments while matching semantic roles to Wikipedia concepts, constructing a weighted semantic graph for each document and linking its sentences (nodes) through the semantic relatedness of the Wikipedia concepts. An iterative ranking algorithm is then applied to the document graphs to extract the most important sentences deemed as the summary. The empirical evaluation of the proposed summarization model on a standard dataset from the Document Understanding Conference (DUC) showed the effectiveness of the approach which outperformed the baseline comparators in terms of ROUGE scores.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016
PublisherIEEE
Pages117-120
Number of pages4
ISBN (Electronic)9781509022519
DOIs
Publication statusPublished - 19 May 2016
Event2nd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2016 - Oxford, United Kingdom
Duration: 29 Mar 20161 Apr 2016

Conference

Conference2nd IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2016
CountryUnited Kingdom
CityOxford
Period29/03/161/04/16

Keywords

  • iterative ranking algorithm
  • Semantic Role Labelling
  • Text summarization
  • Wikipedia concepts

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  • Cite this

    Mohamed, M., & Oussalah, M. (2016). An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts. In Proceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016 (pp. 117-120). [7474363] IEEE. https://doi.org/10.1109/BigDataService.2016.31