Difference between revisions of "An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts"

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{{Infobox work
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| title = An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts
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| date = 2016
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| authors = [[Muhidin Mohamed]]<br />[[Mourad Oussalah]]
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| doi = 10.1109/BigDataService.2016.31
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| link = http://ieeexplore.ieee.org/document/7474363/
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}}
 
'''An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Muhidin Mohamed]] and [[Mourad Oussalah]].
 
'''An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts''' - scientific work related to [[Wikipedia quality]] published in 2016, written by [[Muhidin Mohamed]] and [[Mourad Oussalah]].
  
 
== Overview ==
 
== Overview ==
 
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.
 
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.

Revision as of 06:24, 27 May 2020


An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts
Authors
Muhidin Mohamed
Mourad Oussalah
Publication date
2016
DOI
10.1109/BigDataService.2016.31
Links
Original

An Iterative Graph-Based Generic Single and Multi Document Summarization Approach Using Semantic Role Labeling and Wikipedia Concepts - scientific work related to Wikipedia quality published in 2016, written by Muhidin Mohamed and Mourad Oussalah.

Overview

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.