@inproceedings{bolucu-etal-2024-csiro,
title = "{CSIRO} at Context24: Contextualising Scientific Figures and Tables in Scientific Literature",
author = {B{\"o}l{\"u}c{\"u}, Necva and
Nguyen, Vincent and
Timmer, Roelien and
Yang, Huichen and
Rybinski, Maciej and
Wan, Stephen and
Karimi, Sarvnaz},
editor = "Ghosal, Tirthankar and
Singh, Amanpreet and
Waard, Anita and
Mayr, Philipp and
Naik, Aakanksha and
Weller, Orion and
Lee, Yoonjoo and
Shen, Shannon and
Qin, Yanxia",
booktitle = "Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sdp-1.30",
pages = "314--323",
abstract = "Finding evidence for claims from content presented in experimental results of scientific articles is difficult. The evidence is often presented in the form of tables and figures, and correctly matching it to scientific claims presents automation challenges. The Context24 shared task is launched to support the development of systems able to verify claims by extracting supporting evidence from articles. We explore different facets of this shared task modelled as a search problem and as an information extraction task. We experiment with a range of methods in each of these categories for the two sub-tasks of evidence identification and grounding context identification in the Context24 shared task.",
}
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<abstract>Finding evidence for claims from content presented in experimental results of scientific articles is difficult. The evidence is often presented in the form of tables and figures, and correctly matching it to scientific claims presents automation challenges. The Context24 shared task is launched to support the development of systems able to verify claims by extracting supporting evidence from articles. We explore different facets of this shared task modelled as a search problem and as an information extraction task. We experiment with a range of methods in each of these categories for the two sub-tasks of evidence identification and grounding context identification in the Context24 shared task.</abstract>
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%0 Conference Proceedings
%T CSIRO at Context24: Contextualising Scientific Figures and Tables in Scientific Literature
%A Bölücü, Necva
%A Nguyen, Vincent
%A Timmer, Roelien
%A Yang, Huichen
%A Rybinski, Maciej
%A Wan, Stephen
%A Karimi, Sarvnaz
%Y Ghosal, Tirthankar
%Y Singh, Amanpreet
%Y Waard, Anita
%Y Mayr, Philipp
%Y Naik, Aakanksha
%Y Weller, Orion
%Y Lee, Yoonjoo
%Y Shen, Shannon
%Y Qin, Yanxia
%S Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F bolucu-etal-2024-csiro
%X Finding evidence for claims from content presented in experimental results of scientific articles is difficult. The evidence is often presented in the form of tables and figures, and correctly matching it to scientific claims presents automation challenges. The Context24 shared task is launched to support the development of systems able to verify claims by extracting supporting evidence from articles. We explore different facets of this shared task modelled as a search problem and as an information extraction task. We experiment with a range of methods in each of these categories for the two sub-tasks of evidence identification and grounding context identification in the Context24 shared task.
%U https://aclanthology.org/2024.sdp-1.30
%P 314-323
Markdown (Informal)
[CSIRO at Context24: Contextualising Scientific Figures and Tables in Scientific Literature](https://aclanthology.org/2024.sdp-1.30) (Bölücü et al., sdp-WS 2024)
ACL