L3S at the TREC 2022 CrisisFACTS Track

This paper describes our proposed approach for the multistream summarization of the crisis-related events in the TREC 2022 CrisisFACTS track [2]. We apply a retrieval and ranking-based two-step summarization approach. First, we employ a sparse retrieval framework where content texts from multiple online streams are treated as a document corpus, and a term matching-based retrieval strategy is used to retrieve relevant contents, so-called facts, to the set of queries in a given event day. Next, we use several pre-trained models to measure semantic similarity between query-fact or fact-fact pairs, score and rank the facts for the extraction of daily event summaries.