Implementation of Automatic Text Summarization with TextRank Method in the Development of Al-Qur’an Vocabulary Encyclopedia

Fakhrezi, Muhamad Fahmi; Bijaksana, Moch Arif; Huda, Arief Fatchul

doi:10.1016/j.procs.2021.01.021

Cited by 18 publications

(5 citation statements)

References 7 publications

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“…The training unit in question is the implementation of teaching and learning exercises that are completed on the daily school schedule. So for this situation, all current learning units can be remembered for one public training framework, as long as it fulfills the prerequisites, especially those that interfere with the premise, capacity, and objectives (Al-Saggaf et al, 2014;Librande, 1979;Fakhrezi et al, 2021).…”

Section: National Education Goalmentioning

confidence: 99%

relevance of the message of the Qur'an to the goals of national education

et al. 2022

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Indonesia has a Muslim majority population and has two sources of education that both improve the quality of its citizens' educational resources. The author believes that the two sources, namely the Koran and the national education law, have strong relevance. To prove this assumption, we have conducted a series of studies of educational literature on many literature data published in various educational journals, both national and international. We found the data source through an electronic search on many journals, books, and websites that actively discuss religious and national education issues. After the collection of literature, we further examined the data evaluation system involved in the coding of conclusions drawn after high interpretation to obtain data that answered questions validly and reliably. Based on the data and discussion, we find a high relevance between the messages in the Koran and the goals of national education in Indonesia. It is hoped that this finding will become an essential input for improving educational science that links religious messages and national education.

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Section: National Education Goalmentioning

confidence: 99%

relevance of the message of the Qur'an to the goals of national education

et al. 2022

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“…In Eq. ( 3), 1 k and 2 k denote the conditioning factor constants, and i qf denotes the number of times the word appears in the retrieval process [14]. If the number of times www.ijacsa.thesai.org the word appears in the process of retrieval is 1, the formula can be further simplified, as shown in Eq.…”

Section: ( )mentioning

confidence: 99%

Keyword Acquisition for Language Composition Based on TextRank Automatic Summarization Approach

Jiang,

Xiang,

2024

IJACSA

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It is important to extract keywords from text quickly and accurately for composition analysis, but the accuracy of traditional keyword acquisition models is not high. Therefore, in this study, the Best Match 25 algorithm was first used to preprocess the compositions and evaluate the similarity between sentences. Then, TextRank was used to extract the abstract, construct segmentation and named entity model, and finally verify the research content. The results show that in the performance test, the Best Match 25 similarity algorithm has higher accuracy, recall rate and F1 value, the average running time is only 2182ms, and has the largest receiver working characteristic curve area, which is significantly higher than other models, reaching 0.954. The accuracy of TextRank algorithm is above 90%, the average accuracy of 100 text analysis is 94.23%, the average recall rate and F1 value are 96.67% and 95.85%, respectively. In comparison of the application of the four methods, the research model shows obvious advantages, the average keyword coverage rate is 94.54%, the average processing time of 16 texts is 11.29 seconds, and the average 24-hour memory usage is only 15.67%, which is lower than the other three methods. The experimental results confirm the superiority of the model in terms of keyword extraction accuracy. This research not only provides a new technical tool for language composition teaching and evaluation, but also provides a new idea and method for keyword extraction research in the field of natural language processing.

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“…For example, Li et al [ 26 ] made improvements to TextRank by adding text features. Researchers proposed studies [ 27 , 28 , 29 , 30 ] combining TextRank with different semantic analyses for keyword extraction. Bordoloi et al [ 31 ] proposed a keyword extraction method based on supervised cumulative TextRank, emphasizing the correlation between words from three aspects: edge weight, damping coefficient, and interaction information.…”

Section: Related Workmentioning

confidence: 99%

Multi-Task Learning and Improved TextRank for Knowledge Graph Completion

Tian

Zhang

Wang

et al. 2022

Entropy

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Knowledge graph completion is an important technology for supplementing knowledge graphs and improving data quality. However, the existing knowledge graph completion methods ignore the features of triple relations, and the introduced entity description texts are long and redundant. To address these problems, this study proposes a multi-task learning and improved TextRank for knowledge graph completion (MIT-KGC) model. The key contexts are first extracted from redundant entity descriptions using the improved TextRank algorithm. Then, a lite bidirectional encoder representations from transformers (ALBERT) is used as the text encoder to reduce the parameters of the model. Subsequently, the multi-task learning method is utilized to fine-tune the model by effectively integrating the entity and relation features. Based on the datasets of WN18RR, FB15k-237, and DBpedia50k, experiments were conducted with the proposed model and the results showed that, compared with traditional methods, the mean rank (MR), top 10 hit ratio (Hit@10), and top three hit ratio (Hit@3) were enhanced by 38, 1.3%, and 1.9%, respectively, on WN18RR. Additionally, the MR and Hit@10 were increased by 23 and 0.7%, respectively, on FB15k-237. The model also improved the Hit@3 and the top one hit ratio (Hit@1) by 3.1% and 1.5% on the dataset DBpedia50k, respectively, verifying the validity of the model.

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Implementation of Automatic Text Summarization with TextRank Method in the Development of Al-Qur’an Vocabulary Encyclopedia

Cited by 18 publications

References 7 publications

relevance of the message of the Qur'an to the goals of national education

relevance of the message of the Qur'an to the goals of national education

Keyword Acquisition for Language Composition Based on TextRank Automatic Summarization Approach

Multi-Task Learning and Improved TextRank for Knowledge Graph Completion

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