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Summarizing multiple disaster-relevant data streams simultaneously isparticularly challenging as existing Retrieve&Re-ranking strategies suffer fromthe inherent redundancy of multi-stream data and limited scalability in amulti-query setting. This work proposes an online approach to crisis timelinegeneration based on weak annotation with Deep Q-Networks. It selects on-the-flythe relevant pieces of text without requiring neither human annotations norcontent re-ranking. This makes the inference time independent of the number ofinput queries. The proposed approach also incorporates a redundancy filter intothe reward function to effectively handle cross-stream content overlaps. Theachieved ROUGE and BERTScore results are superior to those of best-performingmodels on the CrisisFACTS 2022 benchmark.

DQNC2S: DQN-based Cross-stream Crisis event Summarizer