Rezumarea automată în inteligența artificială prin învățare nesupravegheată: TextRank

Sfetcu, Nicolae Rezumarea automată în inteligența artificială prin învățare nesupravegheată: TextRank. IT & C, 2023, vol. 2, n. 3, pp. 43-52. [Journal article (Paginated)]

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Alternative locations: https://doi.org/10.58679/IT78864

English abstract

Automatic summarization is the process of summarizing a text document with a computer program to create a summary that captures the most important points of the original document. Technologies that can make a coherent abstract take into account variables such as length, writing style, and syntax. Machine learning is a subfield of artificial intelligence dedicated to understanding and building methods that allow machines to "learn". One key phrase extraction algorithm is TextRank, which exploits the structure of the text itself to determine key phrases that appear "central" to the text.

Romanian abstract

Rezumarea automată este procesul de sumarizare a unui document text cu un program de calculator pentru a crea un rezumat care să rețină cele mai importante puncte ale documentului original. Tehnologiile care pot face un rezumat coerent iau în considerare variabile precum lungimea, stilul de scriere și sintaxa. Învățarea automată este un subdomeniu al inteligenței artificiale dedicat înțelegerii și construirii de metode care permit mașinilor să ”învețe”. Un algoritm de extragere a frazelor cheie este TextRank, careexploatează structura textului în sine pentru a determina expresiile cheie care apar „centrale” pentru text.

Item type: Journal article (Paginated)
Keywords: rezumare, rezumarea automată, învățarea automată, învățarea automată nesupravegheată, TextRank, LexRank
Subjects: L. Information technology and library technology > LP. Intelligent agents.
Depositing user: Nicolae Sfetcu
Date deposited: 08 Oct 2024 08:02
Last modified: 08 Oct 2024 08:02
URI: http://hdl.handle.net/10760/46110

References

Mitchell, Tom (1997). Machine Learning. New York: McGraw Hill. ISBN 0-07-042807-7. OCLC 36417892. Archived from the original on 2020-04-07

George, Gerard; Haas, Martine R.; Pentland, Alex (2014). „FROM THE EDITORS: BIG DATA AND MANAGEMENT”. The Academy of Management Journal. 57 (2): 321–326. ISSN 0001-4273

Hu, Junyan; Niu, Hanlin; Carrasco, Joaquin; Lennox, Barry; Arvin, Farshad (2020). „Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning”. IEEE Transactions on Vehicular Technology. 69 (12): 14413–14423. doi:10.1109/tvt.2020.3034800. ISSN 0018-9545. S2CID 228989788

Yoosefzadeh-Najafabadi, Mohsen; Hugh, Earl; Tulpan, Dan; Sulik, John; Eskandari, Milad (2021). „Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean?”. Front. Plant Sci. 11: 624273. doi:10.3389/fpls.2020.624273. PMC 7835636. PMID 33510761

Bishop, C. M. (2006), Pattern Recognition and Machine Learning, Springer, ISBN 978-0-387-31073-2

Machine learning and pattern recognition „can be viewed as two facets of the same field.”[6]: vii 

Friedman, Jerome H. (1998). „Data Mining and Statistics: What’s the connection?”. Computing Science and Statistics. 29 (1): 3–9.

„What is Machine Learning?”. www.ibm.com. Archived from the original on 2021-08-13

Zhou, Victor (2019-12-20). „Machine Learning for Beginners: An Introduction to Neural Networks”. Medium. Archived from the original on 2022-03-09

Domingos, Pedro (September 22, 2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0465065707, Chapter 6, Chapter 7.

Ethem Alpaydin (2020). Introduction to Machine Learning (Fourth ed.). MIT. pp. xix, 1–3, 13–18. ISBN 978-0262043793.

Rada Mihalcea and Paul Tarau, 2004: TextRank: Bringing Order into Texts, Department of Computer Science University of North Texas „Archived copy” (PDF).

Güneş Erkan and Dragomir R. Radev: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization


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