Metin Özetleme ve Benzerlik Analizi Üzerine Prototip Bir Çalışma: Google Scholar Örneği

Yıl 2025, Cilt: 8 Sayı: 3, 1405 - 1426, 16.06.2025

Onur Şahin , Rıdvan Yayla

https://doi.org/10.47495/okufbed.1570542

Öz

Günümüzde bilgisayar destekli araçlar, akademik araştırmalarda bilgiye erişimi iyileştirme ve verimliliği optimize etmede önemli bir rol oynamaktadır. Bu bağlamda, yapay zeka ve doğal dil işleme tekniklerinin kullanımı, araştırmacıların iş yükünü hafifletmekte ve daha hızlı sonuçlar elde etmelerini sağlamaktadır. Bu çalışmada, araştırma sürecinin özellikle literatür taraması ve kaynak bulma aşamalarında verimliliği artırmayı amaçlayan yeni bir prototip çalışma geliştirilmiştir. Bu çalışma, kullanıcıların girdiği anahtar kelimeler aracılığıyla Google Scholar platformundan belirlenen sayıda akademik makaleyi PDF formatında otomatik olarak indirir. Ardından Berstum, TextRank ve LexRank olmak üzere üç farklı özetleme algoritması seçeneği sunarak, indirilen makalelerin özetlerini çıkarır. Kullanıcı dostu bir arayüz aracılığıyla araştırmacılar istedikleri anahtar kelimeleri girebilir, benzerlik analizi için bir metin sağlayabilir ve tercih ettikleri özetleme algoritmasını seçebilirler. Elde edilen özetler ve benzerlik skorları arayüzde anlaşılır bir şekilde sunulur. İndirilen makalelerin içeriklerini kullanıcının girdiği metinle karşılaştırmak amacıyla metinlerdeki kelimelerin önem ve benzerliğini ölçen TF-IDF (Terim Frekansı-Ters Belge Frekansı) ve kosinüs benzerlik algoritmaları kullanılmıştır. Bu sayede, kullanıcının aradığı konularla ilgili makaleler ve ilgili bölümler tespit edilebilmektedir. Çalışmada ayrıca, geliştirilen prototipin ürettiği özetlerin kalitesini değerlendirmek için, özetlerin kullanıcıların girdiği referans metinlerle olan örtüşmesini, kesinliğini ve anlam bütünlüğünü ölçen Bleu, Rouge ve Meteor metrikleri kullanılmıştır. Bu değerlendirme sonucunda prototip çalışmanın ürettiği özetlerin yüksek doğruluk değerlerine ulaştığı görülmüştür. Prototip bu çalışma, farklı formatlarda ve düzenlerde olabilen PDF dosyalarının yapısal farklılıklarını ele almak için çeşitli ön işleme adımları kullanır. Bu sayede, farklı kaynaklardan gelen makalelerin özetleri ve benzerlik analizleri tutarlı bir şekilde gerçekleştirilebilmektedir.

Anahtar Kelimeler

Doğal Dil İşleme , Benzerlik Analizi , Metin Özetleme , Bertsum , LexRank , TextRank

A Prototype Study on Text Summarization and Similarity Analysis: Google Scholar Example

Öz

Today, computer-aided tools play an important role in improving access to information and optimizing efficiency in academic research. In this context, the use of artificial intelligence and natural language processing techniques reduces the workload of researchers and enables them to obtain faster results. In this study, a new prototype study was developed that aims to increase efficiency, especially in the literature review and source finding stages of the research process. The prototype automatically downloads a specified number of academic articles in PDF format from the Google Scholar platform through the keywords entered by users. Then, it extracts summaries of the downloaded articles by offering three different summarization algorithm options: Bertsum, TextRank and LexRank. Through a user-friendly interface, researchers can enter the desired keywords, provide a text for similarity analysis and select the summarization algorithm they prefer. The resulting summaries and similarity scores are presented in an understandable manner in the interface. TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity algorithms that measure the importance and similarity of words in the texts to compare the content of the downloaded articles with the text entered by the user has been used. In this way, articles and related sections related to the topics searched by the user can be detected. In addition, in order to evaluate the quality of the summaries produced by the developed prototype study, Bleu, Rouge and Meteor metrics were used, which measure the overlap, precision and semantic integrity of the summaries with the reference texts. As a result of this evaluation, it was seen that the summaries produced by the prototype reached high accuracy values. The prototype uses various preprocessing steps to address the structural differences of PDF files that may have different formats and layouts. In this way, summaries and similarity analyses of articles from different sources can be performed consistently.

Anahtar Kelimeler

Natural Language Processing , Similarity Analysis , Text Summarization , Bertsum , LexRank , TextRank

Kaynakça

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