@inproceedings{1daa71c1cb5645219c7201db1a2b786a,
title = "ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts",
abstract = "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 of target 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.",
author = "Murthy, {Sonia K.} and Kyle Lo and Daniel King and Chandra Bhagavatula and Bailey Kuehl and Sophie Johnson and Jonathan Borchardt and Weld, {Daniel S.} and Tom Hope and Doug Downey",
note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",
year = "2022",
language = "الإنجليزيّة",
series = "EMNLP 2022 - 2022 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Demonstrations Session",
publisher = "Association for Computational Linguistics (ACL)",
pages = "200--213",
editor = "Wanxiang Che and Ekaterina Shutova",
booktitle = "EMNLP 2022 - 2022 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Demonstrations Session",
address = "الولايات المتّحدة",
}