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

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

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 - 9th IEEE International Conference on Big Data Science and Engineering, BigDataSE 2015
Publisher	IEEE
Pages	80-87
Number of pages	8
Volume	2
ISBN (Electronic)	9781467379519
DOIs	https://doi.org/10.1109/Trustcom.2015.565
Publication status	Published - 2 Dec 2015
Event	14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015 - Helsinki, Finland Duration: 20 Aug 2015 → 22 Aug 2015

Conference

Conference	14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015
Country/Territory	Finland
City	Helsinki
Period	20/08/15 → 22/08/15

Keywords

knowledge-enriched similarity
named entity relatedness
query-based summarization
Word category subsumption

Access to Document

10.1109/Trustcom.2015.565

Cite this

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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.",

keywords = "knowledge-enriched similarity, named entity relatedness, query-based summarization, Word category subsumption",

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

year = "2015",

month = dec,

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doi = "10.1109/Trustcom.2015.565",

language = "English",

volume = "2",

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booktitle = "Proceedings - 9th IEEE International Conference on Big Data Science and Engineering, BigDataSE 2015",

publisher = "IEEE",

address = "United States",

note = "14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015 ; 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 - 9th IEEE International Conference on Big Data Science and Engineering, BigDataSE 2015. vol. 2, 7345478, IEEE, pp. 80-87, 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, 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 - 9th IEEE International Conference on Big Data Science and Engineering, BigDataSE 2015. Vol. 2 IEEE, 2015. p. 80-87 7345478.

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - Similarity-Based Query-Focused Multi-document Summarization Using Crowdsourced and Manually-built Lexical-Semantic Resources

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AU - Oussalah, Mourad

PY - 2015/12/2

Y1 - 2015/12/2

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.

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KW - named entity relatedness

KW - query-based summarization

KW - Word category subsumption

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ER -

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

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Keywords

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