An Ontology-Based Approach to Text Summarization

Hennig, Leonhard; Umbrath, Winfried; Wetzker, Robert

doi:10.1109/wiiat.2008.175

Cited by 51 publications

(41 citation statements)

References 10 publications

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“…Sentences which are most "close" to the subtopics are then selected. A similar idea but with additional ontology features were proposed by Hennig et al (2008) for sentence scoring. The features they used were tag overlap, subtree depth and subtree count.…”

Section: Knowledge Based Methodmentioning

confidence: 99%

“…In contrast, existing ontology based methods discussed in the literature merely used ontology to identify important concepts in documents. For example, Wu and Liu (2003) perform term based mapping of sentences to ontology to find the most informative concepts in a document, while Hennig et al (2008) classify sentence to nodes on the ontology to identify the main topics in a document. Their efforts were mainly focused on matching the ontology concepts which appear in the text, so that frequent occurring concepts can be labeled as important topics.…”

Section: Proposed Component Based Approachmentioning

confidence: 99%

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Automatic Multi Document Summarization Approaches

Kumar

Salim²

2012

Journal of Computer Science

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Problem statement: Text summarization can be of different nature ranging from indicative summary that identifies the topics of the document to informative summary which is meant to represent the concise description of the original document, providing an idea of what the whole content of document is all about. Approach: Single document summary seems to capture both the information well but it has not been the case for multi document summary where the overall comprehensive quality in presenting informative summary often lacks. It is found that most of the existing methods tend to focus on sentence scoring and less consideration is given to the contextual information content in multiple documents. Results: In this study, some survey on multi document summarization approaches has been presented. We will direct our focus notably on four well known approaches to multi document summarization namely the feature based method, cluster based method, graph based method and knowledge based method. The general ideas behind these methods have been described. Conclusion: Besides the general idea and concept, we discuss the benefits and limitations concerning these methods. With the aim of enhancing multi document summarization, specifically news documents, a novel type of approach is outlined to be developed in the future, taking into account the generic components of a news story in order to generate a better summary.

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Section: Knowledge Based Methodmentioning

confidence: 99%

Section: Proposed Component Based Approachmentioning

confidence: 99%

Automatic Multi Document Summarization Approaches

Kumar

Salim²

2012

Journal of Computer Science

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“…has opened further possibilities in text summarization , and reached increasing attention recently. For example, Henning et al [48] present an approach to sentence extraction that maps sentences to concepts of an ontology. By considering the ontology features, they can improve the semantic representation of sentences which is beneficial in selection of sentences for summaries.…”

Section: Knowledge Bases and Automatic Summarizationmentioning

confidence: 99%

Text Summarization Techniques: A Brief Survey

Allahyari¹,

Pouriyeh²,

Assefi³

et al. 2017

ijacsa

397

175

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Abstract-In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

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“…Some methods start by indexing the words of a text to concepts of a domainrelated taxonomy (i.e., hierarchy of concepts) and explore structural features of the taxonomy (e.g., level) to detect the main subtopics of the text (e.g., WU, LIU, 2003;HENNIG et al, 2008). Sentences or paragraphs that are "closer" to the subtopics are selected to compose the summary.…”

Section: Related Workmentioning

confidence: 99%

Exploring content selection strategies for Multilingual Multi-Document Summarization based on the Universal Network Language (UNL)

Chaud

Felippo

2017

Rev. Est. Ling.

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Multilingual Multi-Document Summarization aims at ranking the sentences of a cluster with (at least) 2 news texts (1 in the user's language and 1 in a foreign language), and select the top-ranked sentences for a summary in the user's language. We explored three concept-based statistics and one superficial strategy for sentence ranking. We used a bilingual corpus (Brazilian Portuguese-English) encoded in UNL (Universal Network Language) with source and summary sentences aligned based on content overlap. Our experiment shows that "concept frequency normalized by the number of concepts in the sentence" is the measure that best ranks the sentences selected by humans. However, it does not outperform the superficial strategy based on the position of the sentences in the texts. This indicates that the most frequent concepts are not always contained in first sentences, usually selected by humans to build the summaries because they convey the main information of the collection. Linguagem, v. 26, n. 1, p. 45-71, 2018 46 Keywords: content selection; concept; statistical measure; multilingual corpus; multi-document summarization. Revista de Estudos da Resumo: O objetivo da Sumarização Automática MultilíngueMultidocumento é ranquear as sentenças de uma coleção com ao menos duas notícias (1 na língua do usuário e 1 em língua estrangeira) e selecionar as mais bem pontuadas para compor um sumário na língua do usuário. Exploramos três estatísticas conceituais e uma estratégia superficial para criar um ranque das sentenças quanto à relevância. Para tanto, utilizamos um corpus bilíngue (português-inglês) anotado via UNL (Universal Network Language) e com textos-fonte e sumários alinhados em nível sentencial. A avaliação indica que a estatística denominada frequência de conceitos normalizada pelo número de conceitos da sentença é a que melhor reproduz o ranqueamento humano. Essa medida, entretanto, não supera a estratégia superficial baseada na posição das sentenças. Isso indica que os conceitos mais frequentes do cluster nem sempre estão contidos nas primeiras sentenças dos textosfonte, usualmente selecionadas pelos humanos para compor os sumários porque veiculam a informação principal da coleção.

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An Ontology-Based Approach to Text Summarization

Cited by 51 publications

References 10 publications

Automatic Multi Document Summarization Approaches

Automatic Multi Document Summarization Approaches

Text Summarization Techniques: A Brief Survey

Exploring content selection strategies for Multilingual Multi-Document Summarization based on the Universal Network Language (UNL)

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