When responding to a disaster, humanitarian experts must rapidly process large amounts of secondary data sources to derive situational awareness and guide decision-making. While these documents contain valuable information, manually processing them is extremely time-consuming when an expedient response is necessary. To improve this process, effective summarization models are a valuable tool for humanitarian response experts as they provide digestible overviews of essential information in secondary data. This paper focuses on extractive summarization for the humanitarian response domain and describes and makes public a new multilingual data collection for this purpose. The collection - called MultiHumES - provides multilingual documents coupled with informative snippets that have been annotated by humanitarian analysts over the past four years. We report the performance results of a recent neural networks-based summarization model together with other baselines. We hope that the released data collection can further grow the research on multilingual extractive summarization in the humanitarian response domain.
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