Sonia Murthy


ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
Sonia Murthy | Kyle Lo | Daniel King | Chandra Bhagavatula | Bailey Kuehl | Sophie Johnson | Jonathan Borchardt | Daniel Weld | Tom Hope | Doug Downey
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Systems that automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge. However, current systems assume a single “best” description per concept, which fails to account for the many ways a concept can be described. We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts. Our system takes advantage of the myriad ways a concept is mentioned across the scientific literature to produce distinct, diverse descriptions oftarget concepts in terms of different reference concepts. In a user study, we find that users prefer (1) descriptions produced by our end-to-end system, and (2) multiple descriptions to a single “best” description. We release the ACCoRD corpus which includes 1,275 labeled contexts and 1,787 expert-authored concept descriptions to support research on our task.