Timely and effective response to humanitarian crises requires quick and accurate analysis of large amounts of text data – a process that can highly benefit from expert-assisted NLP systems trained on validated and annotated data in the humanitarian response domain. To enable creation of such NLP systems, we introduce and release HumSet, a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. The dataset provides documents in three languages (English, French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021 across the globe. For each document, HUMSET provides selected snippets (entries) as well as assigned classes to each entry annotated using common humanitarian information analysis frameworks. HUMSET also provides novel and challenging entry extraction and multi-label entry classification tasks. In this paper, we take a first step towards approaching these tasks and conduct a set of experiments on Pre-trained Language Models (PLM) to establish strong baselines for future research in this domain. The dataset is available at https://blog.thedeep.io/humset/.

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.