Feb. 5, 2024, 3:44 p.m. | Daniele Rege Cambrin Luca Cagliero Paolo Garza

cs.LG updates on arXiv.org arxiv.org

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 …

annotation crisis cs.ai cs.cl cs.ir cs.lg data data streams disaster event fly multiple networks query ranking redundancy scalability strategies summarizing text timeline work

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