43 citations · 64 across the 26 of their papers we have counts for
8 papers · 2 filters
Unsupervised Summarization Re-ranking
Mathieu Ravaut, Shafiq Joty, Nancy Chen
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. Howev…
Are Current Task-oriented Dialogue Systems Able to Satisfy Impolite Users?
Zhiqiang Hu, Roy Kaa-Wei Lee, Nancy F. Chen
Task-oriented dialogue (TOD) systems have assisted users on many tasks, including ticket booking and service inquiries. While existing TOD systems have shown promising performance…
CoHS-CQG: Context and History Selection for Conversational Question Generation
Xuan Long Do, Bowei Zou, Liangming Pan +3
Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditi…
Towards Summary Candidates Fusion
Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown tha…
Multi-Document Summarization with Centroid-Based Pretraining
Ratish Puduppully, Parag Jain, Nancy F. Chen +1
In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary. In this paper, we focus on pretraining objectives for MDS. Spe…
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations
Zhengyuan Liu, Pavitra Krishnaswamy, Nancy F. Chen
There is growing interest in the automated extraction of relevant information from clinical dialogues. However, it is difficult to collect and construct large annotated resources f…