KhabarChin：波斯语重要新闻的自动检测
KhabarChin: Automatic Detection of Important News in the Persian Language
AI总结：
该研究构建含7,869个样本的波斯语重要新闻检测基准Khabarchin，并提出从传统机器学习到transformer的多种模型，同时以弱监督方式检测新闻中的重要句子。
摘要：
了解重要新闻对于保持信息灵通并高效做出明智决策至关重要。自然语言处理（NLP）方法可以显著自动化这一过程。本文介绍了一个此前尚未探索领域中的重要新闻检测，并提出了一个用于检测波斯语重要新闻的新型基准数据集（Khabarchin）。我们将重要新闻文章定义为那些被认为对社会中相当一部分人具有重要意义、能够影响其思维方式或决策的文章。新闻文章来自七家不同的知名波斯语新闻机构，最终标注了7,869个样本并创建了该数据集。研究过程中面临标注分歧较高和类别不平衡这两项挑战，并针对它们给出了解决方案。我们还提出了若干基于学习的模型来处理该任务，范围从传统机器学习到最先进的transformer模型。此外，由于新闻文章通常具有较长的上下文长度，使读者难以识别重要信息，我们引入了第二项任务，即检测新闻文章中的重要句子。我们以弱监督方式识别这些句子。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
