A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms

Mosa, Mohamed Atef; Anwar, Arshad Syed; Hamouda, Alaa

doi:10.1016/j.knosys.2018.09.008

Cited by 53 publications

(33 citation statements)

References 41 publications

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“…Azmi and Altmami (2018) discuss an abstractive summarization approach for Arabic text document, which uses a sentence reduction approach and rhetorical structural theory based sentence extraction approach to generate summary. Mosa et al (2018) present a survey on swarm intelligence (SI) based summarization techniques and report that the usage of SI approaches is quite limited with respect to summarization task. They discuss a summarization framework to cover multiobjective optimization task using SI.…”

Section: Other Methodsmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

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In recent years, an enormous amount of text data from diversified sources has been emerged day-by-day. This huge amount of data carries essential information and knowledge that needs to be effectively summarized to be useful. Hence, the main contribution of this paper is twofold. We first introduce some concepts related to extractive text summarization and then provide a systematic analysis of various text summarization techniques. In particular, some challenges in extractive summarization of single as well as multiple documents are introduced. The problems focus on the textual assessment and similarity measurement between the text documents are addressed. The challenges discussed are generic and applicable to every possible scenario in text summarization. Then, existing state-of-the-art of extractive summarization techniques are discussed that focus on the identified challenges.

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Section: Other Methodsmentioning

confidence: 99%

A Review on Text Summarization Techniques

Verma¹,

Verma²

2020

JSR

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“…Almost all topics characterization likelihood, by certain words, has IDF close to 2 [24], [25], [44]. TF-IDF gives maximum value if rare words have many occurrences in the document [26], [29]. This indicator calculation works as below:…”

Section: Text Relevance By Term Frequency-inverse Document Frequenmentioning

confidence: 99%

“…After the metadata processing, extractive summarization reduces the corpus by removing less necessary text [3], [10]. The potential sentences are either selected through the sentence score algorithm or word embedding principles, i.e., Word2Vec model [11], [18], [29]. Recently, the frequency-based weighting techniques are the preliminary researches in sentence extraction studies [9], [14], [31].…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, the semantic assessment by latent analysis [19], [27], [39], Markov-models, and graph-oriented supervised and unsupervised techniques are under investigation [4], [16], [27]. In short, the extractive-summarization is a key area of research as per the current literature analysis [2], [18], [29], [37]. For simplifying the extractive summarization, the sentence selection process considers the summary-worthiness.…”

Section: Introductionmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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“…Although several works have been devoted to text mining from English and Latin languages [51,52], little attention has been paid to mining the Arabic texts. This is mainly because of the Arabic structural complexity and the presence of several Arabic dialects.…”

Section: Literature Review On Arabic Text Miningmentioning

confidence: 99%

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

2019

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Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.

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A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms

Cited by 53 publications

References 41 publications

A Review on Text Summarization Techniques

A Review on Text Summarization Techniques

Unstructured Text Documents Summarization With Multi-Stage Clustering

A Novel Hybrid Genetic-Whale Optimization Model for Ontology Learning from Arabic Text

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