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undergraduate thesis
Sažimanje teksta zasnovano na modelima dubokog učenja
2021. urn:nbn:hr:168:883229
Mihaljević, Petar
University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

Mihaljević, P. (2021). Sažimanje teksta zasnovano na modelima dubokog učenja (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:883229

Mihaljević, Petar. "Sažimanje teksta zasnovano na modelima dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:883229

Mihaljević, Petar. "Sažimanje teksta zasnovano na modelima dubokog učenja." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2021. https://urn.nsk.hr/urn:nbn:hr:168:883229

Mihaljević, P. (2021). 'Sažimanje teksta zasnovano na modelima dubokog učenja', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 20 March 2025, https://urn.nsk.hr/urn:nbn:hr:168:883229

Mihaljević P. Sažimanje teksta zasnovano na modelima dubokog učenja [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2021 [cited 2025 March 20] Available at: https://urn.nsk.hr/urn:nbn:hr:168:883229

P. Mihaljević, "Sažimanje teksta zasnovano na modelima dubokog učenja", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:168:883229

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Metadata
TitleSažimanje teksta zasnovano na modelima dubokog učenja
Title (english)Text Summarization Based on Deep Learning Models
AuthorPetar Mihaljević
MentorGoran Delač (mentor)
Committee memberGoran Delač (predsjednik povjerenstva)
Committee memberLucija Šikić (član povjerenstva)
Committee memberPetar Afrić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2021-09-01, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Tema rada je bila proučiti primjenu strojnog učenja u sažimanju teksta. Postoje dva glavna tipa sažimanja teksta: ekstrakcijsko i apstrakcijsko sažimanje teksta. Ekstrakcijsko sažimanje teksta izvlači rečenice ili ključne izraze iz teksta koji se na kraju spajaju u sažetak, dok apstrakcijsko sažimanje teksta uključuje parafraziranje i kraćenje dijelova iz teksta. Napravljena je Python aplikacija pomoću TkInter-a koja omogućuje sažimanje teksta na oba načina. Za ekstrakcijsko sažimanje teksta korištene su neke od poznatih programskih knjižnica u Pythonu kao što su SpaCy, Gensim, NLTK, Sumy i slično, dok je za potrebe apstrakcijskog sažimanja teksta dizajnirana i istrenirana neuronska mreža pomoću programskih biblioteka Tensorflow i Keras.
Abstract (english)
The topic of the paper was to study the application of machine learning in text summarization. There are two main types of text summarization: extraction and abstraction based text summarization. Extractive text summarization extracts sentences or key phrases from the text that are then merged into a summary, while abstractive text summarization involves paraphrasing and shortening parts of the text. A Python application was created using TkInter that allows text to be summarized in both ways. Some of the well-known program libraries in Python, such as SpaCy, Gensim, NLTK and Sumy were used for extractive text summarization, while a neural network was designed and trained using Tensorflow and Keras libraries for the purpose of abstractive text summarization.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:883229
Study programmeTitle: Computing
Study programme type: university
Study level: undergraduate
Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
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Created on2023-01-13 15:37:05