@inproceedings{murthy-etal-2022-accord,
title = "{ACC}o{RD}: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts",
author = "Murthy, Sonia and
Lo, Kyle and
King, Daniel and
Bhagavatula, Chandra and
Kuehl, Bailey and
Johnson, Sophie and
Borchardt, Jonathan and
Weld, Daniel and
Hope, Tom and
Downey, Doug",
editor = "Che, Wanxiang and
Shutova, Ekaterina",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = dec,
year = "2022",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-demos.20",
doi = "10.18653/v1/2022.emnlp-demos.20",
pages = "200--213",
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 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.",
}
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%0 Conference Proceedings
%T ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts
%A Murthy, Sonia
%A Lo, Kyle
%A King, Daniel
%A Bhagavatula, Chandra
%A Kuehl, Bailey
%A Johnson, Sophie
%A Borchardt, Jonathan
%A Weld, Daniel
%A Hope, Tom
%A Downey, Doug
%Y Che, Wanxiang
%Y Shutova, Ekaterina
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F murthy-etal-2022-accord
%X 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.
%R 10.18653/v1/2022.emnlp-demos.20
%U https://aclanthology.org/2022.emnlp-demos.20
%U https://doi.org/10.18653/v1/2022.emnlp-demos.20
%P 200-213
Markdown (Informal)
[ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts](https://aclanthology.org/2022.emnlp-demos.20) (Murthy et al., EMNLP 2022)
ACL
- Sonia Murthy, Kyle Lo, Daniel King, Chandra Bhagavatula, Bailey Kuehl, Sophie Johnson, Jonathan Borchardt, Daniel Weld, Tom Hope, and Doug Downey. 2022. ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 200–213, Abu Dhabi, UAE. Association for Computational Linguistics.