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

Abstract

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.