KhabarChin: Automatic Detection of Important News in the Persian
  Language
Hamed Hematian Hemati Arash Lagzian Moein Salimi Sartakhti Hamid Beigy Ehsaneddin Asgari
Being aware of important news is crucial for staying informed and making
well-informed decisions efficiently. Natural Language Processing (NLP)
approaches can significantly automate this process. This paper introduces the
detection of important news, in a previously unexplored area, and presents a
new benchmarking dataset (Khabarchin) for detecting important news in the
Persian language. We define important news articles as those deemed significant
for a considerable portion of society, capable of influencing their mindset or
decision-making. The news articles are obtained from seven different prominent
Persian news agencies, resulting in the annotation of 7,869 samples and the
creation of the dataset. Two challenges of high disagreement and imbalance
between classes were faced, and solutions were provided for them. We also
propose several learning-based models, ranging from conventional machine
learning to state-of-the-art transformer models, to tackle this task.
Furthermore, we introduce the second task of important sentence detection in
news articles, as they often come with a significant contextual length that
makes it challenging for readers to identify important information. We identify
these sentences in a weakly supervised manner.