Zero-Shot Cross-Sentential Scientific Relation Extraction via Entity-Guided Summarization

AbstractStructured information extraction (IE) from scientific abstracts is increasingly leveraging large language models (LLMs). A crucial step in IE is relation extraction (RE), which becomes challenging when entity relations span sentences. Traditional path-based methods, such as shortest dependency paths, are often unable to handle cross-sentential relations effectively. Although LLMs have been utilized as zero-shot learners for IE tasks, they continue to struggle with capturing long-range dependencies and multi-hop reasoning. In this work, we propose using GPT as a zero-shot entity-guided summarizer to encapsulate cross-sentential context into a single-sentence summary for relation extraction. We perform intrinsic evaluations, comparing our approach against direct zero-shot prompting on biomedical scientific abstracts. On the Chemical-Disease Relation (CDR) dataset, our method achieves a 7-point improvement in overall F-score and 6 points for cross-sentential relations. On the Gene-Disease Association (GDA) dataset, we observe an 8-point gain for inter-sentential relations. These results demonstrate that entity-guided summarization with GPT can enhance zero-shot biomedical RE, supporting more effective structured information extraction from scientific texts.