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Nowadays, the Internet is a structure that people can access easily and at the same time produce content easily and without control. In parallel with this situation, the ability of extract information from the raw data that makes up big data has become more complex. The fact that the headlines of the contents contain uncontrolled and misleading elements makes it difficult to reach the right information. The headlines of the contents are important for people to reach the information they want in their limited time. In this study, it is aimed to produce headlines suitable for the content instead of headlines that may be misleading for news. For this purpose, an application that produces headlines for Turkish news with deep learning method has been developed. SuDer news corpus is used as dataset. For the training of the model, it is aimed to obtain more humanoid results in the production of news headlines by using the Transformer architecture, which is frequently preferred in natural language studies today and the abstract summarization method. In this study, in order to compare the performance of the Transformer model, models are prepared and trained with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. At the end of 25 epochs of training with LSTM, GRU and Transformer architectures on the corpus, the values of loss are 1.03, 0.55 and 2.49 respectively. In the experiments performed on the validation data, measurements are made with ROUGE-1, ROUGE-2 and ROUGE-L metrics. As a result of the measurements, it is observed that the Transformer architecture is partially good, based on the metric values produced. In addition, when the headlines produced with these architectures are examined, it is observed that the headline obtained with the Transformer architecture produce headlines that are partially more suitable for the news content compared to other architectures. [ABSTRACT FROM AUTHOR]

In this study, Turkish prospective teachers were asked to summarize the explanations in the Turkish lesson curriculum in order to determine how they summarize an informative text. In the research, "real case study" was used to determine the situations of pre-service teachers about summarizing, and prospective teachers selected through typical case sampling voluntarily participated in the study. Each sentence in the summary texts was read with meaning, and the connection of the sentence with the previous and next sentence or meaning was checked. As a result of this analysis, the themes of "Formal Analysis", "Content", "Grammar-Spelling", "Review", "Summarizing Understandings" and "Reading" were reached. In the findings, it was seen that the student teachers used the title in the summary texts, preferred the purpose behavior in the selection of the title, and the students who preserved the shape features of the main text also preserved the title structure. It was determined that the prospective did not state their purpose of making the summary in the introduction parts of the summary texts and they did not gather the text in the conclusion part. It was determined that the prospective used different text summarization strategies together in creating the content, they adhered to the style of the main text writer, and they were objective in the narration. In the sub-headings of punctuation, spelling and coherence, it was seen that the summary texts of the prospective contained many errors. It was determined that these students' perceptions of the summary text varied, but mostly they thought of the summary as an abbreviated version of the main text. It was determined that the majority of the students read the entire main text while creating the summary text and the summary texts reflected the main text, but the texts that emerged after the summary were not reviewed. [ABSTRACT FROM AUTHOR]