Summarization for Generative Relation Extraction in the Microbiome Domain (2025)

Abstract: We explore a generative relation extraction (RE) pipeline tailored to the study of interactions in the intestinal microbiome, a complex and low-resource biomedical domain. Our method leverages summarization with large language models (LLMs) to refine context before extracting relations via instruction-tuned generation. Preliminary results on a dedicated corpus show that summarization improves generative RE performance by reducing noise and guiding the model. However, BERT-based RE approaches still outperform generative models. This ongoing work demonstrates the potential of generative methods to support the study of specialized domains in low-resources setting.

## introduction the biomedical field is witnessing a rapid expansion in the study of specialized subdomains under the scope of nlp @xcite , such as the microbiome interactions @xcite . the study of the gut microbiome is of high importance @xcite , as it is a key modulator of human health, influencing gastrointestinal diseases like inflammatory bowel disease, metabolic and neurological conditions such as parkinson's disease and depression @xcite @xcite . its role in immune regulation, nutrient metabolism, and the gut-brain axis has made it a focal point for biomedical research. however, the microbiome remains insufficiently characterized, especially at the species interaction level, due to the vast diversity of microbes @xcite , variation in microbial communities from one individual to another, and the scattered, often implicit, nature of knowledge in scientific publications. the areas involving this subdomain are often underrepresented in large-scale annotated corpora, making them inherently low-resource for tasks such as information extraction. among these, relation extraction (re) is essential for structuring biomedical knowledge and supporting downstream applications like knowledge graph construction and hypothesis generation. traditional re methods typically rely on supervised learning with manually annotated data, which is often scarce or unavailable in emerging subdomains. however, the recent rise of llms, pre-trained on extensive corpora, has opened new avenues for addressing low-resource scenarios. these models can be adapted to information extraction task to be performed in a generative manner @xcite . in a low resource setting, the generative paradigm can offer flexibility in dealing with unseen relation types and complex, context-rich inputs. in this work, we explore how such models can be leveraged to improve re in a low-resource biomedical domain, particularly through a generative approach using summarization and instruction tuning. the contributions of this work are as follows: ## related work re is a fundamental task in biomedical nlp, that consists in identifying and classifying semantic relations between entities of interest. since this task is modeled as a classification problem, the dominant approaches have used supervised classification models, such as convolutional neural networks @xcite , recurrent neural networks @xcite , and transformer-based encoders @xcite @xcite . more recently, the emergence of llms has shifted the paradigm from discriminative to generative approaches in re @xcite . for example, asada & fukuda (2024) investigated re, using gpt models, from standard biomedical re datasets, including eu-adr @xcite , gad @xcite , and chemprot @xcite . while their use shows promise, they underperform compared to domain-specific models like biobert @xcite and pubmedbert @xcite . another study proposed a novel re method enhanced by llms, incorporating techniques such as in-context few-shot learning and chain-of-thought reasoning to improve performance @xcite . more specifically, the study of protein-protein interactions as been treated as a binary task @xcite . @xcite compared masked language models with different 2 gpt models using different techniques, and concludes that bert models perform better on complex datasets, while gpt models can be just as effective on simpler ones. chang et al. (2025) investigated the effect of prompt engineering on the same task and showed that domain-specific prompts significantly enhance the performance of two gpt models. however, a key limitation of these generative approaches is that they are usually evaluated on standard biomedical re datasets that are either binary or with a limited number of relation types. @xcite applied their method to several biomedical datasets, including biored @xcite , a manually annotated corpus with eight relation types and four entity types. this makes biored one of the datasets most comparable to our use case in terms of entity and relation diversity. on this dataset, reported f1-scores remained below 10% with gpt-4 and reached 23.2% with openai o1. it is important to note, however, that their evaluation is end-to-end, including both named entity recognition and re, rather than focusing only on relation classification. furthermore, biored does not focus on a specific low-resource subfield. llms have significantly advanced text summarization, moving beyond extractive methods @xcite @xcite to enable fluent and context-aware generation @xcite . modern summarization systems can synthesize information across sentences, rephrase content, and adapt to various domains, including scientific and medical texts @xcite . recent directions further explore personalized @xcite , human-in-the-loop (chen et al., 2023), and real-time summarization, reflecting a shift toward more interactive and user-adaptive systems. these advances highlight the role of llms in redefining summarization as a dynamic and domain-sensitive task. summarization and re share similar challenges, as both require identifying and condensing key information from text @xcite . llms perform well in summarization tasks due to their extensive pretraining on diverse and domain-specific corpora @xcite @xcite , but their effectiveness in structured information extraction tasks like re remains limited @xcite @xcite . the combination of summarization and re, two tasks with clear conceptual overlap in the generative paradigm, remains largely unexplored, especially in the context of closed generative re @xcite . ## methodology we explore two strategies for performing re using llms: a direct approach and a summarizationbased approach. ## conclusion this on-going work investigated the use of llms for biomedical re, introducing summarization as an effective intermediate step. the approach helped reduce textual noise and better align model outputs with structured task requirements, even in low-resource settings. while promising, the study also highlighted persistent challenges such as hallucinations, inconsistent outputs, and difficulty capturing subtle context-dependent relations. these findings point to the need for further refinement in generative re pipelines, particularly in balancing semantic precision with contextual understanding, specifically in low resource specialized context. this study focuses exclusively on the microbiorel dataset, as our primary interest lies in the analysis of interactions within the gut microbiome. at present, microbiorel is one of the few available manually annotated resources having a multi-class setting (with more than ten relation types) and multiple entity types. however, we acknowledge the importance of expanding our evaluation to additional datasets in future work, even if they differ in scope as they could provide valuable perspectives and robustness checks for our approach. moreover, we do not compare our method directly with other generative re approaches, as most of them either address simpler binary tasks or evaluate performance in an end-to-end way by including named entity recognition, making direct comparisons difficult. adapting our pipeline to these settings would be a promising direction for future extensions. another limitation of our study is that we did not generate multiple summaries per passage or apply any selection strategy. instead, we relied on a single generated summary, selected based on its semantic similarity to the original text. future work could explore generating multiple summaries and selecting the most relevant one using task-specific quality criteria. finally, while our summary-based method shows encouraging results, traditional bert-based re models continue to outperform it in terms of raw extraction accuracy. this suggests that further research is needed to enhance the effectiveness of generative models, particularly if they are to offer a computational or practical advantage over current methods. @xcite . large language models for information retrieval: a survey. arxiv preprint arxiv:2308.07107.