Abstract
We consider the problem of automatically creating citations for digital archives. We focus on the learning to cite framework that allows us to create citations without users or experts in the loop. In this work, we study the possibility of learning a citation model on one archive and then applying the model to another archive that has never been seen before by the system.
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Acknowledgments
The work was partially funded by the “Computational Data Citation” (CDC) STARS-StG project of the University of Padua.
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Dosso, D., Setti, G., Silvello, G. (2019). Learning to Cite: Transfer Learning for Digital Archives. In: Manghi, P., Candela, L., Silvello, G. (eds) Digital Libraries: Supporting Open Science. IRCDL 2019. Communications in Computer and Information Science, vol 988. Springer, Cham. https://doi.org/10.1007/978-3-030-11226-4_8
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DOI: https://doi.org/10.1007/978-3-030-11226-4_8
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