  Summarizing multiple disaster-relevant data streams simultaneously is
particularly challenging as existing Retrieve&Re-ranking strategies suffer from
the inherent redundancy of multi-stream data and limited scalability in a
multi-query setting. This work proposes an online approach to crisis timeline
generation based on weak annotation with Deep Q-Networks. It selects on-the-fly
the relevant pieces of text without requiring neither human annotations nor
content re-ranking. This makes the inference time independent of the number of
input queries. The proposed approach also incorporates a redundancy filter into
the reward function to effectively handle cross-stream content overlaps. The
achieved ROUGE and BERTScore results are superior to those of best-performing
models on the CrisisFACTS 2022 benchmark.
