| Форма представления | Статьи в зарубежных журналах и сборниках |
| Год публикации | 2024 |
| Язык | английский |
|
Ахмедова Альфира Мазитовна, автор
|
| Библиографическое описание на языке оригинала |
Akhmedova A, Zhazhneva I, Matrenina O., A System for Processing Large Volumes of Text Information Using Neural Networks//Proceedings - 2024 International Russian Smart Industry Conference, SmartIndustryCon 2024. - 2024. - Vol., Is.. - P.471-476. |
| Аннотация |
This paper presents the implementation of a system for summarizing large amounts of text information using neural networks. Relevance of this system is due to the need to obtain a large amount of knowledge in a very short time. The system works through the telegram bot interface. Telegram bot provides the ability to generate an annotation for input text and output the result as text file. Programming language used is Python. PostgreSQL database management system was chosen to store the data. To implement the system, data was collected, with the help of which the model was further trained. A parser was implemented to collect data. Multilingual mBART model was chosen as the initial model. To evaluate the model of summarization problem, the ROUGE metric was used. The result is a system that allows to obtain reliable information in compressed, accessible form, namely in the form of a text file, for further analysis. |
| Ключевые слова |
text summarization, neural networks, artificial intelligence, telegram bot, transfer learning, text processing |
| Название журнала |
Proceedings - 2024 International Russian Smart Industry Conference, SmartIndustryCon 2024
|
| URL |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193269501&doi=10.1109%2fSmartIndustryCon61328.2024.10515540&partnerID=40&md5=d71cda589f44c5d3096c70cc02b74de3 |
| Пожалуйста, используйте этот идентификатор, чтобы цитировать или ссылаться на эту карточку |
https://repository.kpfu.ru/?p_id=301390 |
Полная запись метаданных  |
| Поле DC |
Значение |
Язык |
| dc.contributor.author |
Ахмедова Альфира Мазитовна |
ru_RU |
| dc.date.accessioned |
2024-01-01T00:00:00Z |
ru_RU |
| dc.date.available |
2024-01-01T00:00:00Z |
ru_RU |
| dc.date.issued |
2024 |
ru_RU |
| dc.identifier.citation |
Akhmedova A, Zhazhneva I, Matrenina O., A System for Processing Large Volumes of Text Information Using Neural Networks//Proceedings - 2024 International Russian Smart Industry Conference, SmartIndustryCon 2024. - 2024. - Vol., Is.. - P.471-476. |
ru_RU |
| dc.identifier.uri |
https://repository.kpfu.ru/?p_id=301390 |
ru_RU |
| dc.description.abstract |
Proceedings - 2024 International Russian Smart Industry Conference, SmartIndustryCon 2024 |
ru_RU |
| dc.description.abstract |
This paper presents the implementation of a system for summarizing large amounts of text information using neural networks. Relevance of this system is due to the need to obtain a large amount of knowledge in a very short time. The system works through the telegram bot interface. Telegram bot provides the ability to generate an annotation for input text and output the result as text file. Programming language used is Python. PostgreSQL database management system was chosen to store the data. To implement the system, data was collected, with the help of which the model was further trained. A parser was implemented to collect data. Multilingual mBART model was chosen as the initial model. To evaluate the model of summarization problem, the ROUGE metric was used. The result is a system that allows to obtain reliable information in compressed, accessible form, namely in the form of a text file, for further analysis. |
ru_RU |
| dc.language.iso |
ru |
ru_RU |
| dc.subject |
text summarization |
ru_RU |
| dc.subject |
neural networks |
ru_RU |
| dc.subject |
artificial intelligence |
ru_RU |
| dc.subject |
telegram bot |
ru_RU |
| dc.subject |
transfer learning |
ru_RU |
| dc.subject |
text processing |
ru_RU |
| dc.title |
A System for Processing Large Volumes of Text Information Using Neural Networks |
ru_RU |
| dc.type |
Статьи в зарубежных журналах и сборниках |
ru_RU |
|
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