Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources

Muhidin A. Mohamed; Mourad Oussalah

doi:10.1109/Trustcom.2015.565

Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources

Muhidin A. Mohamed, Mourad Oussalah

Electronic, Electrical and Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.

Original language	English
Title of host publication	Proceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications
Subtitle of host publication	ISPA 2015
Publisher	IEEE Computational Intelligence Society
Pages	80-87
Volume	3
ISBN (Electronic)	978-1467379519
DOIs	https://doi.org/10.1109/Trustcom.2015.565
Publication status	Published - Aug 2015
Event	13th IEEE International Symposium on Parallel and Distributed Processing with Applications - Finland, Helsinki, Finland Duration: 20 Aug 2015 → 22 Aug 2015

Conference

Conference	13th IEEE International Symposium on Parallel and Distributed Processing with Applications
Country/Territory	Finland
City	Helsinki
Period	20/08/15 → 22/08/15

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10.1109/Trustcom.2015.565Licence: None: All rights reserved

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Mohamed, M. A., & Oussalah, M. (2015). Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources. In Proceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications : ISPA 2015 (Vol. 3, pp. 80-87). IEEE Computational Intelligence Society. https://doi.org/10.1109/Trustcom.2015.565

@inproceedings{2f20328a4a344b7087d65c7278f9512b,

title = "Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources",

abstract = "In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.",

author = "Mohamed, {Muhidin A.} and Mourad Oussalah",

year = "2015",

month = aug,

doi = "10.1109/Trustcom.2015.565",

language = "English",

volume = "3",

pages = "80--87",

booktitle = "Proceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications",

publisher = "IEEE Computational Intelligence Society",

note = "13th IEEE International Symposium on Parallel and Distributed Processing with Applications ; Conference date: 20-08-2015 Through 22-08-2015",

}

Mohamed, MA & Oussalah, M 2015, Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources. in Proceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications : ISPA 2015. vol. 3, IEEE Computational Intelligence Society, pp. 80-87, 13th IEEE International Symposium on Parallel and Distributed Processing with Applications , Helsinki, Finland, 20/08/15. https://doi.org/10.1109/Trustcom.2015.565

Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources. / Mohamed, Muhidin A.; Oussalah, Mourad.
Proceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications : ISPA 2015. Vol. 3 IEEE Computational Intelligence Society, 2015. p. 80-87.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Oussalah, Mourad

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N2 - In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.

AB - In this paper we present an approach for an extractive query focused multi-document summarization which stands on an enhanced knowledge-based short text semantic similarity measures. We incorporate WordNet Taxonomy with Categorial Variation Database (CatVar) and Morphosemantic Links to determine query similarity with sentences and intra-sentences similarities. Besides, we enrich WordNet-derived similarity with named entity semantic relatedness inferred from Wikipedia and underpinned by Normalized Google Distance. We show that our summarizer built primarily on such an improved semantic similarity measure to model relevance, centrality and diversity factors outperforms the best-performing relevant DUC systems and recent closely related studies in at least one or more of the investigated ROUGE metrics. An anti-redundancy mechanism is augmented with the proposed summarizer design using Maximum Marginal Relevance algorithm -MMR.

U2 - 10.1109/Trustcom.2015.565

DO - 10.1109/Trustcom.2015.565

M3 - Conference contribution

VL - 3

SP - 80

EP - 87

BT - Proceedings 13th IEEE International Symposium on Parallel and Distributed Processing with Applications

PB - IEEE Computational Intelligence Society

T2 - 13th IEEE International Symposium on Parallel and Distributed Processing with Applications

Y2 - 20 August 2015 through 22 August 2015

ER -

Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources

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