From Keyterms to Context: Exploring Topic Description Generation in Scientific Corpora (2025)

Abstract: Topic models represent topics as ranked term lists, which are often hard to interpret in scientific domains. We explore Topic Description for Scientific Corpora, an approach to generating structured summaries for topic-specific document sets. We propose and investigate two LLM-based pipelines: Selective Context Summarisation (SCS), which uses maximum marginal relevance to select representative documents; and Compressed Context Summarisation (CCS), a hierarchical approach that compresses document sets through iterative summarisation. We evaluate both methods using SUPERT and multi-model LLM-as-a-Judge across three topic modeling backbones and three scientific corpora. Our preliminary results suggest that SCS tends to outperform CCS in quality and robustness, while CCS shows potential advantages on larger topics. Our findings highlight interesting trade-offs between selective and compressed strategies for topic-level summarisation in scientific domains. We release code and data for two of the three datasets.

## introduction gaining an overview of large scientific corpora is useful for exploring research areas, identifying common methodologies, and tracking emerging developments. a common entry point is topic modeling, which reveals underlying topics and presents them as ordered lists of terms. algorithms such as latent dirichlet allocation (lda; blei (2012)), contextualised topic models (ctm; @xcite ) and bertopic (grootendorst, 2022) are widely used for this purpose. while effective for organising unlabelled data, these methods only provide term-based topic representations, making them difficult to interpret @xcite . most topic modeling pipelines stop at this 1 https://github.com/pierre-achkar/newsumm-25-scs-ccs level, which limits their usefulness for knowledgeintensive tasks, particularly in scientific domains where understanding a research topic requires insight into research goals, methods, and purposes. recent work has sought to improve interpretability by enriching topic representations with machinegenerated labels or short contextual snippets @xcite @xcite . however, these approaches often rely on surface-level signals, lack domain-specific grounding and fail to incorporate document-level context. consequently, they offer limited support for understanding the underlying content of complex domains such as science. in this work, we explore topic description for scientific corpora, an approach that aims to generate structured and informative summaries for topics derived from topic models. these descriptions enrich the topic representation by incorporating document-level context while remaining aligned with the topic terms, offering a clearer view of the underlying research themes. to this end, we propose and investigate two pipelines based on large language models (llms). in both, we expand on prior art for multi-step multidocument generative summarization @xcite into the extreme cases of hundreds (sometimes up to thousands) of documents, while using topic representation as guidance. the first, selective context summarisation (scs), uses maximum marginal relevance (mmr; @xcite ) to select a representative subset of topic documents prior summarisation. the second, compressed context summarisation (ccs), inspired by hierarchical summarisation approaches such as raptor @xcite , applies recursive summarisation over a hierarchy constructed from the topic's documents. we evaluate these pipelines across three topic modeling backbones-ctm @xcite , bertopic @xcite , and top-icgpt @xcite -on three scientific corpora. focusing on reference-free evaluation, we conduct assessment using supert @xcite , a reference-free semantic similarity metric, and a multi-model llm-as-a-judge framework using open-source models. our preliminary results suggest that the mmr-based pipeline consistently produces more focused and concise topic descriptions than the hierarchical approach. we also analyze how topic-level properties, such as size and cohesion, affect topic description quality, and complement our findings typifying topic descriptions characteristics and error sources in each pipeline. ## related work we review prior work on topic modeling, enhanced topic representations, and multi-document scientific summarisation. our work explores connections between these areas by combining topic model outputs with llm-based summarisation to enrich topic representations. ## task formulation we investigate topic description for scientific corpora as an approach to generating structured, interpretable summaries for topic model outputs. given a topic model applied to a scientific corpus d, each topic t k is characterized by: our investigation focuses on generating topic descriptions s k that summarise the main content of d k , remain aligned with w k , and follow a unified structure across topics. each description includes a brief introduction to the topic, followed by the key research objects, methods, and purposes reflected in the underlying documents. this approach facilitates systematic exploration of scientific corpora. for our evaluation, we examine four key quality dimensions. relevance assesses whether descriptions accurately reflect topic aspects by incorporating topic terms meaningfully. factuality examines grounding in original documents without unsupported claims. coherence considers logical flow and consistency in presenting unified topic explanations. fluency evaluates linguistic quality, seeking clear, accessible language that balances readability with technical precision. ## methodology given that the amount of documents in scientific topics may vary from dozens to thousands, we choose to explore methods that are flexible and capable of circumventing llm context window limits. for this end, we propose and investigate two llm-based approaches for generating topic descriptions from sets of documents associated with each topic: selective context summarisation (scs), which uses maximum marginal relevance (mmr; @xcite ) to select a small, diverse subset of representative documents, and compressed context summarisation (ccs), which builds a hierarchical structure over all topic documents using recursive clustering and abstraction, inspired by hierarchical summarisation approaches such as raptor @xcite . both methods operate independently of the underlying topic modeling backbone. in both pipelines, the generation process is guided by the same multi-step prompt-chain template, adapted from the 3a-cot framework @xcite , with topic terms provided as guidance. the full prompts are provided in appendix a. an overview of the pipelines is shown in figure 1 . ## experimental setup to explore the effectiveness and generalisability of the topic description pipelines, we conduct experiments across diverse scientific domains and topic modeling backbones. this section describes the datasets, modeling configurations, and models used for generation and embedding. ## evaluation strategy evaluating topic descriptions presents inherent challenges due to the lack of gold-standard references and the wide variation in topics across different domains. we explore reference-free evaluation metrics that assess quality without requiring human-written summaries. we adopt two complementary strategies: supert, a semantic similarity metric designed for multi-document summarisation, and an llm-as-a-judge framework, which uses prompting-based evaluation with llms. ## results in this section, we present supert scores and mean aspect scores from the llm-asa-judge evaluation on scs and ccs, using qwen-2.5-7b-instruct as our primary model and examining the llm-as-a-judge's consistency across mistral-7b-openorca and gemma-3-27b model families. we first examine overall pipeline effectiveness, then analyze how topic size affects description quality. ## discussion this section explores both pipeline effectiveness and highlights observed trends by topic size and structure. ## conclusion & future work we explored topic description for scientific corpora, an approach to creating structured, documentbased summaries that go beyond term lists. to investigate this, we adapted two llm-based pipelines: selective context summarisation (scs) and compressed context summarisation (ccs). our preliminary findings suggest that scs tends to achieve better performance across datasets and topic modeling backbones, while ccs shows potential advantages for large topics due to its scalable, recursive structure. our observations highlight an interesting trade-off between selective and compressed strategies: scs appears to excel in precision and stability, while ccs may offer broader coverage for large-scale topics. the scaledependent effectiveness patterns we observed suggest that topic size represents an important consideration for practical deployment. together, they provide initial insights for developing interpretable topic representations in scientific domains. this work opens several directions for further exploration, including methodological improvements and practical applications. instead of single-vector retrieval in scs, future work could examine more fine-grained retrieval strategies to improve coverage and adaptability for complex or broad topics.