1.9k citations · 3k across the 69 of their papers we have counts for
10 papers · 2 filters
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization
Yulong Chen, Yang Liu, Ruochen Xu +4
The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization. However, despite the emergence of many summarizatio…
MACSum: Controllable Summarization with Mixed Attributes
Yusen Zhang, Yang Liu, Ziyi Yang +6
Controllable summarization allows users to generate customized summaries with specified attributes. However, due to the lack of designated annotations of controlled summaries, exis…
Task Compass: Scaling Multi-task Pre-training with Task Prefix
Zhuosheng Zhang, Shuohang Wang, Yichong Xu +6
Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language mod…
Generate rather than Retrieve: Large Language Models are Strong Context Generators
Wenhao Yu, Dan Iter, Shuohang Wang +6
Knowledge-intensive tasks, such as open-domain question answering (QA), require access to a large amount of world or domain knowledge. A common approach for knowledge-intensive tas…
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization
Pengcheng He, Baolin Peng, Liyang Lu +11
This paper presents Z-Code++, a new pre-trained language model optimized for abstractive text summarization. The model extends the state of the art encoder-decoder model using thre…
Automatic Rule Induction for Interpretable Semi-Supervised Learning
Reid Pryzant, Ziyi Yang, Yichong Xu +2
Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data. Meanwhile, pretrained transformer models act as black-box correl…
Impossible Triangle: What's Next for Pre-trained Language Models?
Chenguang Zhu, Michael Zeng
Recent development of large-scale pre-trained language models (PLM) have significantly improved the capability of models in various NLP tasks, in terms of performance after task-sp…
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data
Shuohang Wang, Yichong Xu, Yuwei Fang +5
Retrieval-based methods have been shown to be effective in NLP tasks via introducing external knowledge. However, the indexing and retrieving of large-scale corpora bring considera…
AdaPrompt: Adaptive Model Training for Prompt-based NLP
Yulong Chen, Yang Liu, Li Dong +4
Prompt-based learning, with its capability to tackle zero-shot and few-shot NLP tasks, has gained much attention in community. The main idea is to bridge the gap between NLP downst…
Unsupervised Multi-Granularity Summarization
Ming Zhong, Yang Liu, Suyu Ge +7
Text summarization is a user-preference based task, i.e., for one document, users often have different priorities for summary. As a key aspect of customization in summarization, gr…