TY - GEN
T1 - MedICaT
T2 - Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020
AU - Subramanian, Sanjay
AU - Wang, Lucy Lu
AU - Mehta, Sachin
AU - Bogin, Ben
AU - van Zuylen, Madeleine
AU - Parasa, Sravanthi
AU - Singh, Sameer
AU - Gardner, Matt
AU - Hajishirzi, Hannaneh
N1 - Publisher Copyright:
©2020 Association for Computational Linguistics
PY - 2020
Y1 - 2020
N2 - Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MEDICAT, a dataset of medical images in context. MEDICAT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MEDICAT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.
AB - Understanding the relationship between figures and text is key to scientific document understanding. Medical figures in particular are quite complex, often consisting of several subfigures (75% of figures in our dataset), with detailed text describing their content. Previous work studying figures in scientific papers focused on classifying figure content rather than understanding how images relate to the text. To address challenges in figure retrieval and figure-to-text alignment, we introduce MEDICAT, a dataset of medical images in context. MEDICAT consists of 217K images from 131K open access biomedical papers, and includes captions, inline references for 74% of figures, and manually annotated subfigures and subcaptions for a subset of figures. Using MEDICAT, we introduce the task of subfigure to subcaption alignment in compound figures and demonstrate the utility of inline references in image-text matching. Our data and code can be accessed at https://github.com/allenai/medicat.
UR - http://www.scopus.com/inward/record.url?scp=85118434098&partnerID=8YFLogxK
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AN - SCOPUS:85118434098
T3 - Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020
SP - 2112
EP - 2120
BT - Findings of the Association for Computational Linguistics Findings of ACL
PB - Association for Computational Linguistics (ACL)
Y2 - 16 November 2020 through 20 November 2020
ER -