52

 Mazkur maqolada bir toifali matnli hujjatlar to’plamidan tegishli axborot borliklarini chiqarib olish va  yakuniy  hujjatga  ularni  umumlashtirish  masalasi  qaraladi.  Xususan,  Text  Mining  masalaridan  biri hisoblangan  elektron  hujjatlardagi  matnlarni  avtomatik  umumlashtirish  masalasi  hamda  umumlashtirish masalasidagi uslubiyatlaylar batafsil tadqiq etiladi.

  • Ссылка в интернете
  • DOI10.24412/2181-1431-2023-2-37-46
  • Дата создание в систему UzSCI 15-03-2024
  • Количество прочтений 52
  • Дата публикации 20-04-2023
  • Язык статьиO'zbek
  • Страницы37
Ўзбек

 Mazkur maqolada bir toifali matnli hujjatlar to’plamidan tegishli axborot borliklarini chiqarib olish va  yakuniy  hujjatga  ularni  umumlashtirish  masalasi  qaraladi.  Xususan,  Text  Mining  masalaridan  biri hisoblangan  elektron  hujjatlardagi  matnlarni  avtomatik  umumlashtirish  masalasi  hamda  umumlashtirish masalasidagi uslubiyatlaylar batafsil tadqiq etiladi.

Русский

В данной статье рассматривается вопрос извлечения актуальной информации из набора однокатегорийных текстовых документов и ее обобщения в итоговый документ. В частности, подробно изучен вопрос автоматического обобщение текста в электронных документах, который считается одной из задач Text Mining, и методология решение задачи обобщение текста.

English

This article discusses the issue of extracting relevant information from a set of single-category text documents and its summarization into a final document. In particular, the issue of automatic summarization of text in electronic documents, which is considered one of the tasks of Text Mining, and the methodology for solving the problem of summarization of text have been studied in detail.

№ Имя автора Должность Наименование организации
1 Babadjanov E.S. doktarant Muhammad al-Xorazmiy nomidagi TATU
2 Nishnov A.X. professor Muhammad al-Xorazmiy nomidagi TATU
3 Kenjaev X.B. assistent Muhammad alXorazmiy nomidagi TATU Nukus filiali
№ Название ссылки
1 A.X.Nishanov, X.B.Kenjayev, Hujjatlardan jadvallarni chiqarib olish masalasi, usullari va dasturiy ta’minotlar tahlili // Digital Transformation and Artificial Intelligence, ISSN: 3128 -8121. Vol 1, No.2. 2023
2 Bharti, Drsantosh & Babu, Korra, "Automatic Keyword Extraction for Text Summarization: A Survey", 8 February 2017. https://doi.org/10.48550/arXiv.1704.03242
3 E.S.Babajanov, Sh.N.Saidrasulov, X.B.Kenjayev. Algorithm for determining the subject area by formalizing texts in natural Uzbek language // Descendants of Muhammad al-Khwarizmi Scientific-Practical and Information-Analytical Journal. № 2 (24), june 2023. P.54-63
4 G.Erkan, D.R.Radev, “Lexrank: graph-based lexical centrality as salience in text summarization,” Journal of Artificial Intelligence Research, 2004, pp. 457-479.
5 H.Jing. Using hidden Markov modeling to decompose human-written summaries. Comput. Linguist., 2002. 28(4), 527543. doi: 10.1162/089120102762671972
6 Ibrahim, D. (2016). An Overview of Soft Computing. Procedia Computer Science, 102, 34-38. doi: https://doi.org/10.1016/j.procs.2016.09.366
7 M.Gambhir, & V.Gupta, Recent automatic text summarization techniques: a survey. Artificial Intelligence Review, 2017, 47(1), 1-66. doi: 10.1007/s10462-016-9475-9
8 S.Tuarob, S.Bhatia, P.Mitra, & C.L.Giles, AlgorithmSeer: A System for Extracting and Searching for Algorithms in Scholarly Big Data. IEEE Transactions on Big Data, 2016, 2(1), 3-17. doi: 10.1109/TBDATA.2016.2546302
9 S.Wang, X.Zhao, B.Li, B.Ge, D.Tang, Integrating Extractive and Abstractive Models for Long Text Summarization. Paper presented at the 2017 IEEE International Congress on Big Data (BigData Congress).
10 Wafaa S. El-Kassas, Cherif R. Salama, Ahmed A. Rafea, Hoda K. Mohamed Automatic Text Summarization: A Comprehensive Survey. Expert Systems with Applications. July 2020. 165(4):113679. DOI: 10.1016/j.eswa.2020.113679
11 X.B.Kenjayev ,Elektron hujjatlarda jadvallar tuzilishini tanib olish // International Journal of Education, Social Science & Humanities. Finland Academic Research Science Publishers. Vol-11. Issue-7. 2023
12 Zhong, Y., Tang, Z., Ding, X., Zhu, L., Le, Y., Li, K., & Li, K. An Improved LDA Multidocument Summarization Model Based on TensorFlow. Paper presented at the 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI).
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