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undergraduate thesis
Strojno učenje za sintezu sažetaka teksta
2018. urn:nbn:hr:168:402113
Jambrečić, Robert
University of Zagreb
Faculty of Electrical Engineering and Computing

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Jambrečić, R. (2018). Strojno učenje za sintezu sažetaka teksta (Undergraduate thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:402113

Jambrečić, Robert. "Strojno učenje za sintezu sažetaka teksta." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:402113

Jambrečić, Robert. "Strojno učenje za sintezu sažetaka teksta." Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2018. https://urn.nsk.hr/urn:nbn:hr:168:402113

Jambrečić, R. (2018). 'Strojno učenje za sintezu sažetaka teksta', Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 14 April 2024, https://urn.nsk.hr/urn:nbn:hr:168:402113

Jambrečić R. Strojno učenje za sintezu sažetaka teksta [Undergraduate thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2018 [cited 2024 April 14] Available at: https://urn.nsk.hr/urn:nbn:hr:168:402113

R. Jambrečić, "Strojno učenje za sintezu sažetaka teksta", Undergraduate thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:168:402113

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Metadata
TitleStrojno učenje za sintezu sažetaka teksta
Title (english)A Machine-learning Based Model for Text Summarization
AuthorRobert Jambrečić
MentorVladimir Čeperić (mentor)
Committee memberVladimir Čeperić (predsjednik povjerenstva)
Committee memberPetra Bevandić (član povjerenstva)
Committee memberIvan Krešo (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2018-09-05, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
U radu su obrađeni modeli za automatsko sažimanje teksta te je opisan način rada nadziranog i nenadziranog strojnog učenja. Testirano je više algoritama za izvlačenje teksta koji pomoću različitih metoda odlučuju o važnosti rečenica unutar dokumenta. Testiran je i apstrakcijski koder-dekoder model (seq2seq). Objašnjeni su načini evaluacije sažetaka. Svi modeli obrađeni u radu, evaluirani su metodom evaluacije sadržaja.
Abstract (english)
In this thesis models for automatic text summarization are considered. Supervised and unsupervised machine learning operation is described. Various algorithms for extractive summarization were tested which, using different methods, decide about the importance of sentences in a document. The abstractive encoder-decoder model (seq2seq) was tested. Summary evaluation methods are explained. All models described in this thesis were evaluated using the content evaluation method.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:402113
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
Access conditionsClosed access
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Created on2019-03-13 21:08:27