  The volume of information is increasing at an incredible rate with the rapid
development of the Internet and electronic information services. Due to time
constraints, we don't have the opportunity to read all this information. Even
the task of analyzing textual data related to one field requires a lot of work.
The text summarization task helps to solve these problems. This article
presents an experiment on summarization task for Uzbek language, the
methodology was based on text abstracting based on TF-IDF algorithm. Using this
density function, semantically important parts of the text are extracted. We
summarize the given text by applying the n-gram method to important parts of
the whole text. The authors used a specially handcrafted corpus called "School
corpus" to evaluate the performance of the proposed method. The results show
that the proposed approach is effective in extracting summaries from Uzbek
language text and can potentially be used in various applications such as
information retrieval and natural language processing. Overall, this research
contributes to the growing body of work on text summarization in
under-resourced languages.
