# Multi Stage Retrieval for Web Search During Crisis
## 1 Introduction  
The exponential growth of digital information, while enhancing global connectivity and facilitating access to knowledge, presents formidable challenges in efficiently retrieving relevant and accurate data. Users are frequently overwhelmed by the volume of content on various online platforms, such as social media, news outlets, and blogs, particularly when seeking timely insights during rapidly evolving events [1]. Crisis scenarios, such as natural disasters (e.g., wildfires, hurricanes, floods) [2], industrial accidents, or public health emergencies (e.g., COVID-19) [3], exemplify these dynamic contexts. In such situations, the ability to access precise and pertinent information is not only beneficial but can critically influence outcomes, including resource allocation, coordination of rescue efforts, and the effective guidance of affected populations, often making the difference in life-saving decisions. Despite the critical need, existing information retrieval (IR) methods exhibit significant limitations when applied to crisis management. Traditional lexical search approaches, such as BM25, while computationally efficient and effective for keyword-based queries, often struggle with semantic nuances, synonymy, and the implicit meaning prevalent in crisis-related communication, leading to low precision or missed relevant documents [4]. Conversely, while recent advancements in large pre-trained natural language processingmodels [5] and generative models [6; 7] have demonstrated improved semantic understanding, they introduce their own set of drawbacks. These pre-trained models, used to implement dense retrieval systems, though powerful, can be computationally intensive for real-time processing of high-volume data streams. Furthermore, these models often require domain-specific fine-tuning to adapt to the specialized vocabulary and context of crisis events, as their performance can degrade when dealing with data uncommon in general-purpose training corpora [8; 9; 10]. Generative models, while capable of producing fluent text and employed in other solutions [11; 12], pose risks of hallucination and information fabrication that are unacceptable in crisis scenarios [13]. Moreover, their high computational demands or reliance on commercial APIs make them impractical for many emergency response organizations operating under resource constraints. To overcome these limitations, this paper introduces a novel multi-stage, fully extractive retrieval framework specifically designed for crisis event information management. Our approach integrates the efficiency of traditional lexical search with the semantic understanding of modern dense retrieval methods in a cascaded architecture. This design first employs a lexical retrieval stage for broad candidate generation, followed by a dense retrieval stage for precise reranking, thereby optimizing both recall and precision. This synergy allows our framework to outperform the current best competitor by 13% in BERT-Score F1-Score. Crucially, our system also achieves a 5% reduction in search latency compared to single-stage dense retrieval methods, while concurrently ensuring superior average recall. This enhanced performance provides a robust, scalable, and computationally viable solution tailored to the demands of real-world crisis response operations. Our contributions can be summarized as follows:  
* **Architectural Design for Crisis-Specific Retrieval:** We propose a novel multi-stage retrieval architecture that synergistically combines a lexical retrieval stage for efficient, broad recall with a subsequent dense retrieval stage for high-precision reranking. This cascaded design is specifically tailored to handle the high-volume, redundant, noisy, and time-sensitive data characteristic of crisis events, improving single-stage systems and generic multi-stage designs. * **LLM Independency:** Our framework achieves state-of-the-art performance without relying on computationally expensive large language models (LLMs).