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.
@article{arxiv.2401.06683,
title = {DQNC2S: DQN-based Cross-stream Crisis event Summarizer},
author = {Daniele Rege Cambrin and Luca Cagliero and Paolo Garza},
journal= {arXiv preprint arXiv:2401.06683},
year = {2024}
}