NLM IRP Seminar Schedule
UPCOMING SEMINARS
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May 21, 2024 Ziynet Kesimoglu
Multiomics Data Integration using Graph Convolutional Networks -
May 23, 2024 Leslie Ronish
Identification of fold-switching proteins by FLIM-FRET -
May 28, 2024 Harutyun Saakyan
TBD -
May 30, 2024 Deepak Gupta
TBD
RECENT SEMINARS
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May 14, 2024 Stanley Liang
Knowledge-driven Latent Diffusion For COVID-19 Pneumonia Radiology Pattern Synthesis -
May 9, 2024 Pascal Mutz
The Riboviria protein structurome expands virus protein annotation and highlights protein relations -
April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome
Scheduled Seminars on Nov. 1, 2022
Contact NLM_IRP_Seminar_Scheduling@mail.nih.gov with questions about this seminar.
Abstract:
Although a growing amount of health-related literature has been made available to a large audience online, the language of scientific articles can be difficult for the general public to comprehend. Thus, simplifying and adapting this expert-level language into plain language versions is needed for the public to reliably understand the vast health-related literature. Machine and Deep Learning algorithms for automatic adaptation are a possible solution; however, gold standard datasets are needed to properly evaluate their performances. Current datasets consist of either pairs of comparable professional- and general public-facing documents or pairs of semantically similar sentences mined from such documents. This creates a trade-off between imperfect alignments and small test sets. To address this issue, we created the Plain Language Adaptation of Biomedical Abstracts dataset. This dataset is the first manually adapted dataset that is both document- and sentence-aligned. It contains 750 adapted abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we benchmark state-of-the-art Deep Learning approaches on the dataset, setting baselines for future research.