The proliferation of data and text documents such as articles, web pages,
books, social network posts, etc. on the Internet has created a fundamental
challenge in various fields of text processing under the title of "automatic
text summarisation". Manual processing and summarisation of large volumes of
textual data is a very difficult, expensive, time-consuming and impossible
process for human users. Text summarisation systems are divided into extractive
and abstract categories. In the extractive summarisation method, the final
summary of a text document is extracted from the important sentences of the
same document without any modification. In this method, it is possible to
repeat a series of sentences and to interfere with pronouns. However, in the
abstract summarisation method, the final summary of a textual document is
extracted from the meaning and significance of the sentences and words of the
same document or other documents. Many of the works carried out have used
extraction methods or abstracts to summarise the collection of web documents,
each of which has advantages and disadvantages in the results obtained in terms
of similarity or size. In this work, a crawler has been developed to extract
popular text posts from the Instagram social network with appropriate
preprocessing, and a set of extraction and abstraction algorithms have been
combined to show how each of the abstraction algorithms can be used.
Observations made on 820 popular text posts on the social network Instagram
show the accuracy (80%) of the proposed system.

本研究开发了一个爬虫来提取 Instagram 社交网络上受欢迎的文本文章，并结合一组抽取和抽象算法来展示如何使用每个抽象算法，观察 820 个受欢迎的文本文章在社交网络上的准确性（80％）。