Multi-Query Focused Disaster Summarization via Instruction-Based Prompting
<|reference_start|>Multi-Query Focused Disaster Summarization via Instruction-Based Prompting: Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on multi-stream fact-finding with a focus on web sources such as Twitter, Reddit, Facebook, and Webnews. Here, participants are asked to develop systems that can extract key facts from several disaster-related events, which ultimately serve as a summary. This paper describes our method to tackle this challenging task. We follow previous work and propose to use a combination of retrieval, reranking, and an embarrassingly simple instruction-following summarization. The two-stage retrieval pipeline relies on BM25 and MonoT5, while the summarizer module is based on the open-source Large Language Model (LLM) LLaMA-13b. For summarization, we explore a Question Answering (QA)-motivated prompting approach and find the evidence useful for extracting query-relevant facts. The automatic metrics and human evaluation show strong results but also highlight the gap between open-source and proprietary systems.<|reference_end|>
@article{seeberger2024multi-query,
    title={Multi-Query Focused Disaster Summarization via Instruction-Based
  Prompting},
    author={Philipp Seeberger, Korbinian Riedhammer},
    journal={arXiv preprint arXiv:2402.09008},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.09008},
    primaryClass={cs.CL}
}