Learning to generate problem-specific abstractions from candidate papers significantly improves finding relevant scientific inspirations, especially for distant domains that share transferable principles rather than surface-level topics.
This paper tackles scientific inspiration retrieval by learning to extract transferable abstract principles from papers that are specifically tailored to a target research problem.