3.7k citations · 8.3k across the 22 of their papers we have counts for
5 papers · 1 filter
MeanSum: A Neural Model for Unsupervised Multi-document Abstractive Summarization
Eric Chu, Peter J. Liu
Abstractive summarization has been studied using neural sequence transduction methods with datasets of large, paired document-summary examples. However, such datasets are rare and…
Learning to Write Notes in Electronic Health Records
Peter J. Liu
Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, r…
Generating Wikipedia by Summarizing Long Sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot +4
We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify sa…
Scalable and accurate deep learning for electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen +31
Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical model…
Beyond Word Importance: Contextual Decomposition to Extract Interactions from LSTMs
W. James Murdoch, Peter J. Liu, Bin Yu
The driving force behind the recent success of LSTMs has been their ability to learn complex and non-linear relationships. Consequently, our inability to describe these relationshi…