Ekstraktivno sažimanje teksta pomoću dubokog učenja

master's thesis

• Title (english): Extractive Text Summarization Using Deep Learning
• Author: Kristijan Vrbanc
• Mentor: Marin Šilić (mentor)
• Committee member: Marin Šilić (predsjednik povjerenstva)
• Committee member: Klemo Vladimir (član povjerenstva)
• Committee member: Goran Delač (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-05, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U ovom radu dan je pregled nad područjem obrade prirodnog jezika kroz opis odabranih modela dubokog učenja korištenih kroz povijest. Opisani su postojeći pristupi za semantičko sažimanja teksta, a naglasak je stavljen na ekstraktivno sažimanje. Kao praktični ishod rada, ostvaren je web sustav za ekstraktivno sažimanje teksta. Dan je opis arhitekture i programskog ostvarenja sustava. Sustav se zasniva na modelu BERT i algoritmu grupiranja k-sredina. Ekstraktivni sažetak se stvara slijedom operacija. Ulazni tekst se prvo dijeli na rečenice. Pomoću modela BERT se računaju vektorski prikazi rečenica. Vektorski prikazi rečenica se grupiraju algoritmom k-sredina, a zatim se odabiru rečenice koje ulaze u sažetak. Ostvareni sustav vrednovan je mjerama ROUGE na skupovima podataka CNN/Daily Mail i Newsroom.

Abstract (english)

This paper provides an overview of the natural language processing field through a description of selected deep learning models used throughout history. Existing approaches to semantic text summarization are described, with an emphasis on extractive text summarization. As a practical outcome of the paper, a web system for extractive text summarization is realized. A description of the architecture and software implementation of the system is given. The system is based on the BERT model and the k-means clustering algorithm. An extractive summary is created using a sequence of operations. The input text is first divided into sentences. Using the BERT model, vector representations of sentences are calculated. Vector representations of sentences are clustered using the k-means algorithm, followed by the selection of the sentences that enter the summary. The realized system is evaluated using the ROUGE metric on CNN/Daily Mail and Newsroom datasets.

• Keywords: obrada prirodnog jezika; sažimanje teksta; ekstraktivno sažimanje; duboko učenje; BERT; grupiranje k-sredina
• Keywords (english): natural language processing; text summarization; extractive summarization; deep learning; BERT; k-means clustering
• Language: croatian
• URN:NBN: urn:nbn:hr:168:782307
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 16:28:51