System of automatic scientific article summarization in Turkish

The widespread use of the internet today, along with the rapidly increasing information, has brought along great information pollution. it has become a big problem for internet users to obtain meaningful data from this large and noisy data. Text summarization, which is generally used on texts obtained from digital media, has also been used for summarizing scientific articles in different fields. in this study, a scientific text summary study was carried out to be used on Turkish articles written in the field of informatics. A large Turkish Informatics Literature dataset was created with the articles collected from Dergipark. in addition to the text pre-processing studies available in the literature on this dataset, a new original pre-processing function has been developed by the scientific article format. While summarizing, Deep Belief Networks (DBN), which has an increasing use in the field of natural language processing in the literature, has been used. To measure the performance of the developed system, reference summaries were created with the BERT algorithm, which is a pre-trained natural language processing model. After the scientific articles were summarized with BERT and Deep Belief Networks, the abstracts were compared with BERT Score and BART Score, a specialized comparison metric of the BERT Model. The results showed that the developed Turkish informatics Literature Summarization Method constitutes a summary of a scientific article with 0.78 F-Score and 0.68 BART Score in the BERT Score metric.