75 citations · 144 across the 5 of their papers we have counts for
7 papers · 1 filter
Calibrating Sequence likelihood Improves Conditional Language Generation
Yao Zhao, Misha Khalman, Rishabh Joshi +3
Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained mod…
SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization
Yao Zhao, Mohammad Saleh, Peter J. Liu
Most prior work in the sequence-to-sequence paradigm focused on datasets with input sequence lengths in the hundreds of tokens due to the computational constraints of common RNN an…
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh +1
Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summariza…
SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders
Peter J. Liu, Yu-An Chung, Jie Ren
We propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which w…
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…