Ontology-based text summarization. The case of Texminer

Hipola, Pedro; Senso, José A.; Leiva-Mederos, Amed; Domínguez-Velasco, Sandor

doi:10.1108/lht-01-2014-0005

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Hípola et al. (2014) consider this kind of summarization process using the frequency of the terms as statistical, considering the words' relevances to determine sentence strengths.…”

Section: Texts Collection Methods and Technologiesmentioning

confidence: 99%

Towards corpora creation from social web in Brazilian Portuguese to support public security analyses and decisions

Carvalho

Costa²

2022

LHT

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PurposeThis article presents two Brazilian Portuguese corpora collected from different media concerning public security issues in a specific location. The primary motivation is supporting analyses, so security authorities can make appropriate decisions about their actions.Design/methodology/approachThe corpora were obtained through web scraping from a newspaper's website and tweets from a Brazilian metropolitan region. Natural language processing was applied considering: text cleaning, lemmatization, summarization, part-of-speech and dependencies parsing, named entities recognition, and topic modeling.FindingsSeveral results were obtained based on the methodology used, highlighting some: an example of a summarization using an automated process; dependency parsing; the most common topics in each corpus; the forty named entities and the most common slogans were extracted, highlighting those linked to public security.Research limitations/implicationsSome critical tasks were identified for the research perspective, related to the applied methodology: the treatment of noise from obtaining news on their source websites, passing through textual elements quite present in social network posts such as abbreviations, emojis/emoticons, and even writing errors; the treatment of subjectivity, to eliminate noise from irony and sarcasm; the search for authentic news of issues within the target domain. All these tasks aim to improve the process to enable interested authorities to perform accurate analyses.Practical implicationsThe corpora dedicated to the public security domain enable several analyses, such as mining public opinion on security actions in a given location; understanding criminals' behaviors reported in the news or even on social networks and drawing their attitudes timeline; detecting movements that may cause damage to public property and people welfare through texts from social networks; extracting the history and repercussions of police actions, crossing news with records on social networks; among many other possibilities.Originality/valueThe work on behalf of the corpora reported in this text represents one of the first initiatives to create textual bases in Portuguese, dedicated to Brazil's specific public security domain.

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“…Hípola et al. (2014) consider this kind of summarization process using the frequency of the terms as statistical, considering the words' relevances to determine sentence strengths.…”

Section: Texts Collection Methods and Technologiesmentioning

confidence: 99%

Towards corpora creation from social web in Brazilian Portuguese to support public security analyses and decisions

Carvalho

Costa²

2022

LHT

View full text Add to dashboard Cite

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“…In ontology-based approaches, knowledge resources such as WordNet (Miller 1995;Fellbaum 1998), DBPedia (Bizer et al 2009), or other domain-specific ontologies are used to create abstractive summaries (Mohan et al 2016). Several ontology-based abstractive systems have been developed that extract concepts or key phrases from a text, creating a summary (Lee, Jian, and Huang 2005;Hennig, Umbrath, and Wetzker 2008;Baralis et al 2013;Hípola et al 2014). In this work, a pre-defined ontology of concepts is utilized for content generalization in the pre-processing phase and for concept matching in the post-processing phase, as it is going to be described in the following sections.…”

Section: Related Workmentioning

confidence: 99%

Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization

Kouris

Alexandridis

Stafylopatis

2021

Computational Linguistics

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Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence to sequence neural-based text summarization along with structure and semantic-based methodologies. The proposed framework is capable of dealing with the problem of out-of-vocabulary or rare words, improving the performance of the deep learning models. The overall methodology is based on a well defined theoretical model of knowledge-based content generalization and deeplearning predictions for generating abstractive summaries. The framework is comprised of three key elements: (i) a pre-processing task, (ii) a machine learning methodology and (iii) a post-processing task. The pre-processing task is a knowledge-based approach, based on ontological knowledge resources, word-sense-disambiguation and namedentity recognition, along with content generalization, that transforms ordinary text into a generalized form. A deep learning model of attentive encoder-decoder architecture, which is expanded to enable a coping and coverage mechanism, as well as reinforcement learning and transformer-based architectures, is trained on a generalized version of text-summary pairs, learning to predict summaries in a generalized form. The post-processing task utilizes knowledge resources, word embeddings, word-sense disambiguation and heuristic algorithms based on text similarity methods in order to transform the generalized version of a predicted summary to a final, humanreadable form. An extensive experimental procedure on three popular datasets evaluates key aspects of the proposed framework, while the obtained results exhibit promising performance, validating the robustness of the proposed approach.

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