Federated Learning (FL) is a novel machine learning approach that allows the model trainer to access more data samples, by training the model across multiple decentralized data sources, while data access constraints are in place.
Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets.
Query Focused Summarization (QFS) has been addressed mostly using extractive methods.
Ranked #1 on Query-Based Extractive Summarization on Debatepedia
In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes.