Query-oriented text summarization based on multiobjective evolutionary algorithms and word embeddings

Fors-Isalguez, Yanet; Valadez, Jorge Hermosillo; Montes-y-Gómez, Manuel

doi:10.3233/jifs-169506

Cited by 8 publications

(5 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The degree of progress for Spanish was ascertained using the ranking of the state-of-theart methods and systems for the AETS shown in Table 10. Based on the results shown in this paper, the opportunity to generate new research is opened using the TER corpus to try state-of-the-art methods as [55][56][57][58][59][60][61][62], among others Also, the methods and systems tested in this paper could be adjusted their parameters to obtain better results in SAETS.…”

Section: The Ranking Results Of the State-of-the-art Methods And Systemmentioning

confidence: 99%

Ground Truth Spanish Automatic Extractive Text Summarization Bounds

Mendoza¹,

Ledeneva²,

Hernández³

et al. 2020

CyS

View full text Add to dashboard Cite

The textual information has accelerated growth in the most spoken languages by native Internet users, such as Chinese, Spanish, English, Arabic, Hindi, Portuguese, Bengali, Russian, among others. It is necessary to innovate the methods of Automatic Text Summarization (ATS) that can extract essential information without reading the entire text. The most competent methods are Extractive ATS (EATS) that extract essential parts of the document (sentences, phrases, or paragraphs) to compose a summary. During the last 60 years of research of EATS, the creation of standard corpus with human-generated summaries and evaluation methods which are highly correlated with human judgments help to increase the number of new state-of-the-art methods. However, these methods are mainly supported for the English language, leaving aside other equally important languages such as Spanish, which is the second most spoken language by natives and the third most used on the Internet. A standard corpus for Spanish EATS (SAETS) is created to evaluate the state-of-the-art methods and systems for the Spanish language. The main contribution consists of a proposal for configuration and evaluation of 5 state-ofthe-art methods, five systems and four heuristics using three evaluation methods (ROUGE, ROUGE-C, and Jensen-Shannon divergence). It is the first time that Jensen-Shannon divergence is used to evaluate AETS. In this paper the ground truth bounds for the Spanish language are presented, which are the heuristics baseline:first, baseline:random, topline and concordance. In addition, the ranking of 30 evaluation tests of the state-of-the-art methods and systems is calculated that forms a benchmark for SAETS.

show abstract

Section: The Ranking Results Of the State-of-the-art Methods And Systemmentioning

confidence: 99%

Ground Truth Spanish Automatic Extractive Text Summarization Bounds

Mendoza¹,

Ledeneva²,

Hernández³

et al. 2020

CyS

View full text Add to dashboard Cite

show abstract

“… Text Analysis Conference (TAC) [57] : TAC is a group of evaluation workshops that aim to advance research in Natural Language Processing and related applications.…”

Section: B Text Summarization Datasets and Evaluation Metricsmentioning

confidence: 99%

Query-Focused Multi-document Summarization Survey

Alanzi,

Alballaa

2023

IJACSA

View full text Add to dashboard Cite

With the exponential growth of textual information on the web and in multimedia, query-focused multi-document summarization (QFMS) has emerged as a critical research area. QFMS aims to generate concise summaries that address user queries and satisfy their information needs. This paper provides a comprehensive survey of state-of-the-art approaches in QFMS, focusing specifically on graph-based and clustering-based methods. Each approach is examined in detail, highlighting its advantages and disadvantages. The survey covers ranking algorithms, sentence selection techniques, redundancy removal methods, evaluation metrics, and available datasets. The principal aim of this paper is to present a thorough analysis of QFMS approaches, providing researchers and practitioners with valuable insights into the field. By surveying existing techniques, the paper identifies the challenges and issues faced in QFMS and discusses potential future directions. Moreover, the paper emphasizes the importance of addressing coherency, ambiguity, vague references, evaluation methods, redundancy, and diversity in QFMS. Performance standards and competing approaches are also discussed, showcasing the advancements made in QFMS. The paper acknowledges the need for improving summarization coherence, readability, and semantic efficiency, while balancing compression ratios and summarizing quality. Additionally, it highlights the potential of hybrid methods and the integration of extractive and abstractive techniques to achieve more humanlike summaries.

show abstract

“…This section mainly reviews studies focusing on detection of fake news [16]- [24], [4]. However, a few example studies pertaining to sentiment classification using BERT and/or SVM [5], [25], [26], text summarization via multi-objective optimization [27], [28] are also covered considering that either BERT/SVM is used or evolutionary/multi-objective optimization approaches are used in such studies.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Fors-Isalguez et al [27] proposed a method to generate text summarization based on the query by using word embedding and evolutionary algorithms. Pre-processing of data was done by cleaning and removing unrelated stop words from the text.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved FakeBERT for Fake News Detection

Ali,

Gulzar

2023

Applied Computer Systems

View full text Add to dashboard Cite

In the present era of the internet and social media, the way of information dissemination has changed. However, due to rapid growth in the amount of news generated regularly and the unsupervised nature of social media, fake news turns out to be a big problem. Fake news can easily build a false positive or negative perception about a person, or an event. Fake news was also used as a tool by propagandists during the Coronavirus (COVID-19) pandemic. Thus, there is a need to use technology to tag fake news and prevent its dissemination. Previously, different algorithms were designed to detect fake news but without considering the semantic meaning and long sentence dependence. This research work proposes a new approach to the detection of fake news in the context of COVID-19. The suggested approach uses a combination of Bidirectional Encoder Representations from Transformers (BERT) for extracting context meaning from sentences, SVM for pattern identification to detect fake news in a better way from the COVID-19 dataset, and an evolutionary algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to distribute text for Support Vector Machine (SVM) classification. The suggested approach improves accuracy by 5.2 % by removing a certain amount of ambiguity from sentences.

show abstract

Query-oriented text summarization based on multiobjective evolutionary algorithms and word embeddings

Cited by 8 publications

References 19 publications

Ground Truth Spanish Automatic Extractive Text Summarization Bounds

Ground Truth Spanish Automatic Extractive Text Summarization Bounds

Query-Focused Multi-document Summarization Survey

An Improved FakeBERT for Fake News Detection

Contact Info

Product

Resources

About