  We present an overview of the CLEF-2019 Lab ProtestNews on Extracting
Protests from News in the context of generalizable natural language processing.
The lab consists of document, sentence, and token level information
classification and extraction tasks that were referred as task 1, task 2, and
task 3 respectively in the scope of this lab. The tasks required the
participants to identify protest relevant information from English local news
at one or more aforementioned levels in a cross-context setting, which is
cross-country in the scope of this lab. The training and development data were
collected from India and test data was collected from India and China. The lab
attracted 58 teams to participate in the lab. 12 and 9 of these teams submitted
results and working notes respectively. We have observed neural networks yield
the best results and the performance drops significantly for majority of the
submissions in the cross-country setting, which is China.

Computation and Language