R e TAG : Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking (2025)

Abstract: Recent advances in question answering have led to substantial progress in tasks such as multi-hop reasoning. However, global sensemaking—answering questions by synthesizing information from an entire corpus—remains a significant challenge. A prior graph-basedapproach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs. To address these limitations, we propose ReTAG, a RetrievalEnhanced, Topic-Augmented Graph framework that constructs topic-specific subgraphs and retrieves the relevant summaries for response generation. Experiments show that ReTAG improves response quality while significantly reducing inference time compared to the baseline. Our code is available at https://github.com/bykimby/retag.

## introduction global sensemaking @xcite refers to the task of synthesizing comprehensive information across a large corpus of documents to generate a coherent response to a query. for instance, consider the global sensemaking query in fig. 1 : 'based on the recent movie plot corpus, analyze the trends in the protagonist's narrative development.' answering this question requires examining not only character-event relations within a single movie plot document but also shared and divergent narrative patterns across movie plot corpus. such queries require analyzing the interdependencies among diverse entities (e.g., characters, events) across the entire corpus, which makes global sensemaking a highly challenging task. in contrast, local traditional qa targets fact-based queries with specific documents, such as identifying which girl group performed golden in k-pop demon hunters in fig. 1 @xcite @xcite . unlike such localized approaches, global sensemaking requires integrating a broader range of knowledge, often spanning across multiple sources. the task of global sensemaking presents several challenges. a key issue is that the vast amount of information within the corpus d cannot be directly processed by a large language model (llm) due to its limited context window @xcite . identifying the relevant entities and relations for a given query and representing their complex interdependencies in a way that is both efficient and meaningful for the llm is an ongoing challenge. a recent baseline approach for global sensemaking, proposed by @xcite , uses a graphbased method that captures the relationships among entities across the entire corpus. the method constructs a contextualized entity-relation graph and generates community summaries by clustering related subgraphs. while effective, this method relies on a single general set of summaries for all queries, making it less flexible and potentially less efficient for capturing specific details or nuances in global sensemaking tasks. additionally, it struggles with handling large document corpora due to the high complexity of processing the entire graph. to address these challenges, we propose retag, a retrieval-enhanced, topic-augmented graphbased approach for global sensemaking. retag builds upon the baseline graph-based approach by incorporating topic-augmented summarization and retrieval augmentation techniques to provide a more efficient and effective response generation process. through our experiments, we demonstrate that retag not only improves the quality of the responses but also significantly reduces inference time, making it a robust solution for global sensemaking in large-scale document corpora. our contributions are three-fold: ## related work global sensemaking early multi-document summarization methods focused on generating concise summaries from document sets @xcite , while recent work has explored modeling inter-document relationships using neural topic models @xcite and graph-based representations @xcite @xcite . these studies focus on summarization but do not extend to question answering. @xcite advances global sensemaking for qa but uses a single general set of summaries for all queries. in contrast, we enhance global sensemaking by incorporating topic-augmented summarization and retrieval-based filtering, improving both effectiveness and efficiency. retrieval-augmented qa qa research has focused on retrieval-augmented approaches that identify the most relevant document for a given query, evaluated across various benchmark datasets @xcite @xcite @xcite . methods like realm @xcite , rag @xcite , and self-rag @xcite use retrievers to find a closely aligned document and generate answers based on it. building on this, approaches such as ircot @xcite , react @xcite , and rescore @xcite extend the retrieval process to multiple documents for more complex reasoning. however, these methods focus on subsets of the corpus and lack the ability to capture global sense, which is the focus of this work. graph-based qa a line of research has explored graph-based qa approaches that model relations among entities across documents. several studies @xcite @xcite leverage graph structures to identify reasoning paths and retrieve relevant context for qa. kg-gpt @xcite , g-retriever @xcite and grag @xcite perform reasoning over query-focused subgraphs constructed from retrieved graph elements. however, graph-based qa approaches for global sensemaking have yet to be fully explored. @xcite addresses this challenge by building a graph that captures the context of the entire corpus. our work extends this method by incorporating topic-augmented graph construction and community retrieval, enhancing both response quality and efficiency. ## methods given a query q and a document database d, global sensemaking is defined as the task of generating a response a that synthesizes comprehensive information from the entire corpus of documents d. this task involves identifying the relations among entities (people, places, and events, etc) relevant to the given query q within the entire corpus d, and integrating this information to produce a coherent and informed response. this task contrasts to existing qa and multi-hop qa tasks, performing detail-oriented qa on a subset of documents. this characteristic poses a significant challenge, as it is practically infeasible to incorporate d into the limited context window of llms. furthermore, identifying the entities and relations pertinent to the query q from d, and conveying their complex inter-dependencies in a textual form that the llm can efficiently process remains a significant and ongoing challenge. to address these challenges, we propose retag, a retrieval-enhanced, topic-augmented graphbased global sensemaking approach. building on the prior work of graph-based global sensemaking @xcite , which is outlined in sec. 3.1, we introduce topic and retrieval augmentation techniques in sec. 3.2 and sec. 3.3 , respectively. finally, sec. 3.4 presents retag, our complete framework that integrates both techniques. ## conclusion we introduce retag, a retrieval-enhanced, topic-augmented graph-based global sensemaking approach over large document corpora. by integrating topic-augmented summarization and retrieval-based answer generation with keyword expansion, retag overcomes key limitations of prior graphbased methods. our experiments show that retag improves global sensemaking while significantly reducing inference time.