21 citations · 28 across the 5 of their papers we have counts for
3 papers · 1 filter
Entity-level Factual Adaptiveness of Fine-tuning based Abstractive Summarization Models
Jongyoon Song, Nohil Park, Bongkyu Hwang +4
Abstractive summarization models often generate factually inconsistent content particularly when the parametric knowledge of the model conflicts with the knowledge in the input doc…
Shuffle & Divide: Contrastive Learning for Long Text
Joonseok Lee, Seongho Joe, Kyoungwon Park +4
We propose a self-supervised learning method for long text documents based on contrastive learning. A key to our method is Shuffle and Divide (SaD), a simple text augmentation algo…
Enhancing Semantic Understanding with Self-supervised Methods for Abstractive Dialogue Summarization
Hyunjae Lee, Jaewoong Yun, Hyunjin Choi +2
Contextualized word embeddings can lead to state-of-the-art performances in natural language understanding. Recently, a pre-trained deep contextualized text encoder such as BERT ha…