DQNC2S: DQN-based Cross-stream Crisis event Summarizer

• URL: http://arxiv.org/abs/2401.06683v2
• Date: Fri, 2 Feb 2024 09:54:18 GMT
• Title: DQNC2S: DQN-based Cross-stream Crisis event Summarizer
• Authors: Daniele Rege Cambrin, Luca Cagliero, Paolo Garza
• Abstract summary: 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. The achieved ROUGE and BERTScore results are superior to those of best-performing models on the CrisisFACTS 2022 benchmark.
• Score: 12.522889958051284
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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