Naslov
Duboko učenje za ekstraktivno sažimanje teksta
(Deep Learning for Extractive Text Summarization)

Autori
Aljević, Dino

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Odjel za informatiku

Mjesto
Rijeka

Datum
09.11

Godina
2021

Stranica
30

Mentor
Martinčić-Ipšić, Sanda

Ključne riječi
sažimanje teksta, neuronska mreža, duboko učenje, višeslojni perceptron, rekurentna neuronska mreža, upravljačka rekurentna jedinica
(ext summarization, neural network, deep learning, multilayer perceptron, recurrent neural network, gated recurrent unit)

Sažetak
Extractive text summarization is tasked with the automatic creation of a summary by extracting the most salient sentences from the original text. In this thesis, two extractive summarization methods are trained and tested: multilayer perceptron and gated recurrent unit. Both methods are trained as a binary classifier capable of assigning the class summary or not-summary to input sentences. In the multilayer perceptron method, each sentence is represented by Doc2Vec embedding. The embedding layer of GRU is initialized with Word2Vec vectors of the words in the vocabulary and the input vectors contain sequences of the words from the original text. The output is a class probability assigned by the logistic function classifier. The CNN/DailyMail dataset is used to train and evaluate extractive summarization models using the ROUGE-1, ROUGE-2, and ROUGE-LCS measures to assess the performance. Generally, GRU achieves better extractive summarization results when precision is considered, while perceptron performs better according to the recall metrics, regardless of the used ROUGE measures. The results are indicating that both methods are capable of performing extractive summarization task.

Izvorni jezik
Hrvatski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti

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