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master's thesis
Apstraktivno sažimanje teksta pomoću dubokog učenja
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:155666
Tomić, Josipa
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Tomić, J. (2022). Apstraktivno sažimanje teksta pomoću dubokog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:155666

Tomić, Josipa. "Apstraktivno sažimanje teksta pomoću dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:155666

Tomić, Josipa. "Apstraktivno sažimanje teksta pomoću dubokog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:155666

Tomić, J. (2022). 'Apstraktivno sažimanje teksta pomoću dubokog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 21 June 2024, https://urn.nsk.hr/urn:nbn:hr:168:155666

Tomić J. Apstraktivno sažimanje teksta pomoću dubokog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 June 21] Available at: https://urn.nsk.hr/urn:nbn:hr:168:155666

J. Tomić, "Apstraktivno sažimanje teksta pomoću dubokog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:155666

Metadata
TitleApstraktivno sažimanje teksta pomoću dubokog učenja
Title (english)Abstractive Text Summarization Using Deep Learning
AuthorJosipa Tomić
MentorMarin Šilić (mentor)
Committee memberMarin Šilić (predsjednik povjerenstva)
Committee memberKlemo Vladimir (član povjerenstva)
Committee memberGoran Delač (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-09-14, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U ovom su radu opisani neki od modela dubokog učenja korištenih u zadatku apstraktivnog sažimanja teksta. Ti se modeli temelje na sequence-to-sequence arhitekturi izgrađenoj od dva dijela: neuronske mreže za koder i druge mreže za dekoder. RNN koder – dekoder modeli su poboljšani dvosmjernim RNN-om i LSTM jedinicama kako bi se mogli koristiti u području sažimanja teksta. U radu je opisana Transformer arhitektura koja koristi samo mehanizam pažnje bez rekurentnih mreža kako bi ubrzala proces učenja. Za zadatak apstraktivnog sažimanja teksta, fino je podešen model BART, model koji je proizašao iz standardne arhitektura Transformera. U finom prilagođavanju korišten je skup podataka na hrvatskom jeziku dobiven strojnim prevođenjem CNN/Daily mail skupa.
Abstract (english)
This paper describes some of the deep learning models used in the abstract text summarization task. These models are based on a sequence-to-sequence architecture constructed of two parts: neural network for the encoder and neural network for the decoder. RNN encoder - decoder models have been enhanced with bidirectional RNN and LSTM units to be used in the text summarization area. The paper describes a Transformer architecture that uses only the attention mechanism without recurrent networks to speed up the learning process. For the task of abstract text summarization, the BART model is fine-tuned. BART is a model derived from the standard Transformer architecture. In a fine-tuning process, a dataset obtained by machine translation of the CNN/ Daily mail dataset into Croatian was used.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:155666
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 14:03:21