This paper presents Daleel 2026, the first shared task on argument mining in Arabic news editorials and competitive debates. Daleel includes two tasks: Task 1 identifies which of six argumentative discourse unit (ADU) categories are present in a paragraph, while Task 2 locates the units with their labels and exact text spans. The tasks are performed using a new corpus of 30 documents, 1,042 paragraphs, and about 76k words. Twenty-nine teams registered, 12 participated, and 10 submitted system papers. Both tasks have Closed and Open tracks. The best results for the Closed track were 0.7117 macro-F1 in Task 1 and 0.7547 modified overlap F1 in Task 2. The results show different insights: the best Closed-track score of each team was higher on debates than on editorials. A broad range of approaches was used in Task 1 submissions, including Arabic encoders, large language models, rules, and ensembles. Task 2 systems, in particular the top-ranked ones, were more similar and mainly used CRF or Viterbi decoding with combinations of models or training runs. The rare ADU categories remained difficult in both tasks, and identifying exact span boundaries was an additional challenge in Task 2.

This paper introduces the Corpus of Arabic Competitive Debates (Munazarat). Despite the significance of competitive debating as an activity of fostering critical thinking and promoting dialogue, researchers within the fields of Arabic Natural Language Processing (NLP), linguistics, argumentation studies, and education have access to very limited datasets about competitive debating. At this study stage, we introduce Munazarat 1.0, which combines recordings of approximately 50 hours collected from 73 debates at QatarDebate-recognized tournaments, where all of those debates were available on YouTube. Munazarat is a novel specialized speech Arabic corpus, mostly in Modern Standard Arabic (MSA), consisting of diverse debating topics and showing rich metadata for each debate. The transcription of debates was done using Fenek, a speech-to-text Kanari AI tool, and three native Arabic speakers reviewed each transcription file to enhance the quality provided by the machine. The Munazarat 1.0 dataset can be used to train Arabic NLP tools, develop an argumentation mining machine, and analyze Arabic argumentation and rhetoric styles. Keywords: Arabic Speech Corpus, Modern Standard Arabic, Debates