Development of Text Summarization Method based on Graph Theory and Malatya Centrality Algorithm

Yıl 2023, Cilt: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Sayı: IDAP-2023, 90 - 102, 18.10.2023

Selman Yakut Cevher Tayyib Bakan

https://doi.org/10.53070/bbd.1350971

Öz

With the advancement of the internet, humanity has gained easy access to a plethora of information. However, to access accurate content, numerous texts and sources must be read. These texts often contain repetitive words and sentences. The abundance of information renders reading texts in their entirety inefficient in terms of time and makes finding suitable content challenging. To overcome these difficulties, various methods have been developed in research on automatic summarization. In the literature, there are numerous methods developed for different purposes in text summarization. Nevertheless, text summarization can generally be divided into two distinct categories: extractive and abstractive summarization. Abstractive algorithms tend to create new sentences by learning from the text. However, this approach prolongs the working process due to the learning phase and the generated sentences may not possess absolute accuracy. On the other hand, extractive methods, if unable to generate new sentences, have the ability to provide faster and completely accurate summaries by selecting sentences that already exist in the text. For these reasons, in our study, the aim is to perform text summarization using graph theory and the Malatya Centrality Algorithm. The Malatya Centrality Algorithm offers a polynomial approach to solving Vertex Cover Problems and is regarded as an effective solution method. It is believed that the Malatya Centrality Algorithm will contribute to graph-based text summarization. The implementation has been developed using the Python programming language, and the obtained results have been evaluated.

Anahtar Kelimeler

Graph-Based Text Summarization, Malatya Centrality Algorithm, Text Summarization

Kaynakça

• Yakut S, Oztemiz F, Karci A(07.12.2022 ). A New Approach Based on Centrality Value in Solving the Minimum Vertex Cover Problem: Malatya Centrality Algorithm. Computer Science. Volume Vol:7, Issue Issue:2, 81 - 88.
• Hark C, Taner Uçkan T, Seyyarer E. , Karcı A(30.09.2019). Metin Özetlemesi için Düğüm Merkezliklerine Dayalı Denetimsiz Bir Yaklaşım dergipark, 8(3).
• Hark C, Taner Uçkan T, Karcı A(29.06.2022). A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity
• Erhandı, B. (2020). Derin Öğrenme ile Metin Özetleme
• Tülek, M. (2007). Türkçe için Metin Özetleme
• Kaynar, O., IŞIK, Y. E., GÖRMEZ, Y., & DEMİRKOPARAN, F. (2017). Genetic Algorithmn Based Sentence Extraction For Automatic Text Summarization. dergipark, 3(2).
• Khushboo S. Thakkar, R.V. Dharaskar, & M.B. Chandak. (2010). Graph-Based Algorithms for Text Summarization. IEEE. 10.1109/ICETET.2010.104
• Güneş Erkan, & Dragomir R. Radev. (2004). LexRank: Graph-based Lexical Centrality as Saliencein Text Summarization.
• Ibrahim F. Moawad, & Mostafa Aref. (2013). Semantic graph reduction approach for abstractive Text Summarization. IEEE. 10.1109/ICCES.2012.6408498
• Rafael Ferreira, Frederico Freitas, Luciano de Souza Cabral, Rafael Dueire Lins, Rinaldo Lima, Gabriel França, Steven J. Simskez, & Luciano Favaro. (2013). A Four Dimension Graph Model for Automatic Text Summarization. IEEE. 10.1109/WI-IAT.2013.55
• Joel Larocca Neto, Alex A. Freitas, & Celso A. A. Kaestner. (2003). Automatic Text Summarization Using a Machine Learning Approach. springer.
• Karel Ježek, & Josef Steinberger. (2007). Automatic Text Summarization (The state of the art 2007 and new challenges).
• Chirantana Mallick, Ajit Kumar Das, Madhurima Dutta, Asit Kumar Das, & Apurba Sarkar. (2018). Graph-Based Text Summarization Using Modified TextRank. springer.
• Rada Mihalcea. (2004). Graph-based Ranking Algorithms for Sentence Extraction, Applied to Text Summarization.
• Makbule Gulcin Ozsoy, & Ferda Nur Alpaslan. (2011). Text summarization using Latent Semantic Analysis. 10.1177/0165551511408848
• Yogesh Sankarasubramaniam, Krishnan Ramanathan, & Subhankar Ghosh. (2014). Text summarization using Wikipedia. sciencedirect.
• Rasim ALGULIEV, & Ramiz ALIGULIYEV. (2009). Evolutionary Algorithm for Extractive Text Summarization. Scientific Research.
• Naresh Kumar Nagwani, & Dr. Shrish Verma. (2011). A Frequent Term and Semantic Similarity based Single Document Text Summarization Algorithm. researchgate.
• Raed Z. Al-Abdallah, & Ahmad T. Al-Taani. (2017). Arabic Single-Document Text Summarization Using Particle Swarm Optimization Algorithm. sciencedirect.
• S.A. Babar, & Pallavi D. Patil. (2015). Improving Performance of Text Summarization. sciencedirect.
• Arti Jain, Anuja Arora, Jorge Morato, Divakar Yadav, & Kumar Vimal Kumar. (2022). Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm. mdpi.
• Carlos N. Silla Jr., Gisele L. Pappa, Alex A. Freitas, & Celso A. A. Kaestner. (2004). Automatic Text Summarization with Genetic Algorithm-Based Attribute Selection. springer.
• Sumya Akter, Aysa Siddika Asa, Md. Palash Uddin, Md. Delowar Hossain, Shikhor Kumer Roy, & Masud Ibn Afjal. (2017). An extractive text summarization technique for Bengali document(s) using K-means clustering algorithm. IEEE. 10.1109/ICIVPR.2017.7890883