  Relevant and timely information collected from social media during crises can
be an invaluable resource for emergency management. However, extracting this
information remains a challenging task, particularly when dealing with social
media postings in multiple languages. This work proposes a cross-lingual method
for retrieving and summarizing crisis-relevant information from social media
postings. We describe a uniform way of expressing various information needs
through structured queries and a way of creating summaries answering those
information needs. The method is based on multilingual transformers embeddings.
Queries are written in one of the languages supported by the embeddings, and
the extracted sentences can be in any of the other languages supported.
Abstractive summaries are created by transformers. The evaluation, done by
crowdsourcing evaluators and emergency management experts, and carried out on
collections extracted from Twitter during five large-scale disasters spanning
ten languages, shows the flexibility of our approach. The generated summaries
are regarded as more focused, structured, and coherent than existing
state-of-the-art methods, and experts compare them favorably against summaries
created by existing, state-of-the-art methods.
