Ontology-based text summarization. The case of Texminer

Purpose – The purpose of this paper is to look into the latest advances in ontology-based text summarization systems, with emphasis on the methodologies of a socio-cognitive approach, the structural discourse models and the ontology-based text summarization systems. Design/methodology/approach – The...

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Bibliographic Details
Published in:Library hi tech 2014-06, Vol.32 (2), p.229-248
Main Authors: Hipola, Pedro, A Senso, Jose, Leiva-Mederos, Amed, Dominguez-Velasco, Sandor
Format: Article
Language:English
Subjects:
Algorithms
Civil engineering
Coastal engineering
Computer programs
Design engineering
Documents
Information retrieval
Knowledge representation
Librarianship/library management
Library & information science
Library technology
Linguistics
Natural language processing
Ontology
Pragmatism
Recall
Semantics
Sentences
Statistical methods
Studies
Texts
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Summary:Purpose – The purpose of this paper is to look into the latest advances in ontology-based text summarization systems, with emphasis on the methodologies of a socio-cognitive approach, the structural discourse models and the ontology-based text summarization systems. Design/methodology/approach – The paper analyzes the main literature in this field and presents the structure and features of Texminer, a software that facilitates summarization of texts on Port and Coastal Engineering. Texminer entails a combination of several techniques, including: socio-cognitive user models, Natural Language Processing, disambiguation and ontologies. After processing a corpus, the system was evaluated using as a reference various clustering evaluation experiments conducted by Arco (2008) and Hennig et al. (2008). The results were checked with a support vector machine, Rouge metrics, the F-measure and calculation of precision and recall. Findings – The experiment illustrates the superiority of abstracts obtained through the assistance of ontology-based techniques. Originality/value – The authors were able to corroborate that the summaries obtained using Texminer are more efficient than those derived through other systems whose summarization models do not use ontologies to summarize texts. Thanks to ontologies, main sentences can be selected with a broad rhetorical structure, especially for a specific knowledge domain.
ISSN:0737-8831
2054-166X
DOI:10.1108/LHT-01-2014-0005