University of Oulu

Muhidin Mohamed, Mourad Oussalah, SRL-ESA-TextSum: A text summarization approach based on semantic role labeling and explicit semantic analysis, Information Processing & Management, Volume 56, Issue 4, 2019, Pages 1356-1372, ISSN 0306-4573, https://doi.org/10.1016/j.ipm.2019.04.003

SRL-ESA-TextSum : a text summarization approach based on semantic role labeling and explicit semantic analysis

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Author: Mohamed, Muhidin1; Oussalah, Mourad2
Organizations: 1Computer Science, School of Engineering and Applied Sciences, Aston Universty, Aston Triangle, Birmingham B4 7ET, UK
2Centre for Ubiquitous Computing, Faculty of Information Technology Computer Science, University of Oulu, P.O. Box 4500, 90014 Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe2020042322174
Language: English
Published: Elsevier, 2019
Publish Date: 2021-04-15
Description:

Abstract

Automatic text summarization attempts to provide an effective solution to today’s unprecedented growth of textual data. This paper proposes an innovative graph-based text summarization framework for generic single and multi document summarization. The summarizer benefits from two well-established text semantic representation techniques; Semantic Role Labelling (SRL) and Explicit Semantic Analysis (ESA) as well as the constantly evolving collective human knowledge in Wikipedia. The SRL is used to achieve sentence semantic parsing whose word tokens are represented as a vector of weighted Wikipedia concepts using ESA method. The essence of the developed framework is to construct a unique concept graph representation underpinned by semantic role-based multi-node (under sentence level) vertices for summarization. We have empirically evaluated the summarization system using the standard publicly available dataset from Document Understanding Conference 2002 (DUC 2002). Experimental results indicate that the proposed summarizer outperforms all state-of-the-art related comparators in the single document summarization based on the ROUGE-1 and ROUGE-2 measures, while also ranking second in the ROUGE-1 and ROUGE-SU4 scores for the multi-document summarization. On the other hand, the testing also demonstrates the scalability of the system, i.e., varying the evaluation data size is shown to have little impact on the summarizer performance, particularly for the single document summarization task. In a nutshell, the findings demonstrate the power of the role-based and vectorial semantic representation when combined with the crowd-sourced knowledge base in Wikipedia.

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Series: Information processing & management
ISSN: 0306-4573
ISSN-E: 1873-5371
ISSN-L: 0306-4573
Volume: 56
Issue: 4
Pages: 1356 - 1372
DOI: 10.1016/j.ipm.2019.04.003
OADOI: https://oadoi.org/10.1016/j.ipm.2019.04.003
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
Copyright information: © 2019 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.
  https://creativecommons.org/licenses/by-nc-nd/4.0/