New Summary Text Generation from Long Texts With BiLSTM Deep Learning Method

Year 2024, Volume: 14 Issue: 3, 1096 - 1119, 15.09.2024

Onur Şahin , Rıdvan Yayla

https://doi.org/10.31466/kfbd.1423022

Abstract

Nowadays, the integration of deep learning techniques into creative text generation processes is widely used among researchers and software developers. Deep learning is known for its ability to learn complex information over large data sets, and this feature offers significant advantages in language understanding and text generation. The aim of the study is to derive a new summary text by training the expressions in a long text on the basis of Bidirectional Long Short-Term Memory (BiLSTM) deep learning architecture. For this purpose, two documents of different lengths and types (Novel, Personal Development) written in Turkish were used as a dataset, and the texts in the dataset were subjected to a series of pre-processes such as data cleaning, tokenization and vectorization. The study evaluated other deep learning architectures such as LSTM, GRU, BiGRU and CNN, as well as BiLSTM, and found that the BiLSTM model had the highest METEOR, BLEU and ROGUE scores in two different book types and different word counts (1,000, 2,000 and 5,000 words). showed. These findings show that BiLSTM produces more successful results than other models for text summarization and text generation. The method of generating creative and original texts from a certain novel or personal development book using BiLSTM is an inspiring resource for researchers and software developers, and it is envisaged that the proposed method can be applied for different text types. In this way, it has been shown that the BiLSTM architecture produces successful results in text summarization and generation processes.

Keywords

BiLSTM, Natural language processing, Text generation, Deep learning

BiLSTM Derin Öğrenme Yöntemi ile Uzun Metinlerden Yeni Özet Metinlerin Türetilmesi

Abstract

Günümüzde, derin öğrenme tekniklerinin yaratıcı metin oluşturma süreçlerine entegrasyonu, araştırmacılar ve yazılım geliştiriciler arasında yaygın olarak kullanılmaktadır. Derin öğrenme, büyük veri setleri üzerinde karmaşık bilgileri öğrenme yeteneği ile bilinir ve bu özellik, dil anlama ve metin üretme konularında önemli avantajlar sunar. Çalışmanın amacı Bidirectional Long Short-Term Memory (BiLSTM) derin öğrenme mimarisi temelinde uzun bir metindeki ifadelerin eğitilerek yeni bir özet metnin türetilmesidir. Bu amaç doğrultusunda Türkçe dilinde yazılmış farklı uzunlukta ve türdeki (Roman, Kişisel Gelişim) iki doküman veriseti olarak kullanılmış, veri setindeki metinler veri temizleme, tokenizasyon ve vektörleştirme gibi bir dizi önişlemden geçirilmiştir. Çalışma, BiLSTM'nin yanı sıra LSTM, GRU, BiGRU ve CNN gibi diğer derin öğrenme mimarilerini de değerlendirmiş ve BiLSTM modelinin iki farklı kitap türünde ve farklı kelime sayılarında (1.000, 2.000 ve 5.000 kelime) en yüksek METEOR, BLEU ve ROGUE skorlarına sahip olduğunu ortaya koymuştur. Bu bulgular, BiLSTM’nin metin özetleme ve metin üretme için diğer modellere göre daha başarılı sonuçlar ürettiğini göstermektedir. BiLSTM kullanarak belli bir roman veya kişisel gelişim kitabından yaratıcı ve özgün metinler türetme yöntemi araştırmacılar ve yazılım geliştiriciler için ilham verici bir kaynak olup, önerilen yöntemin farklı metin türleri için de uygulanabileceği öngörülmektedir. Bu sayede, metin özetleme ve üretme süreçlerinde BiLSTM mimarisinin başarılı sonuçlar ürettiği gösterilmiştir.

Keywords

BiLSTM, Doğal dil işleme, Metin üretme, Derin öğrenme

Ethical Statement

Yapılan çalışmada araştırma ve yayın etiğine uyulmuştur.

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