activity
20212023
most citedRobust Deep Reinforcement Learning for Extractive Legal Summarization

26 citations · 27 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2023★ 1 cited

Universal Graph Continual Learning

Thanh Duc Hoang, Do Viet Tung, Duy-Hung Nguyen +3

We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution. Whereas prior studies primarily tackle one setting of…

cs.IR2022

Improving Document Image Understanding with Reinforcement Finetuning

Bao-Sinh Nguyen, Dung Tien Le, Hieu M. Vu +3

Successful Artificial Intelligence systems often require numerous labeled data to extract information from document images. In this paper, we investigate the problem of improving t…

cs.IR2022

HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System

Bao-Sinh Nguyen, Quang-Bach Tran, Tuan-Anh Nguyen Dang +2

Measuring the confidence of AI models is critical for safely deploying AI in real-world industrial systems. One important application of confidence measurement is information extra…

cs.AI2022

Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback

Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen +4

For summarization, human preference is critical to tame outputs of the summarizer in favor of human interests, as ground-truth summaries are scarce and ambiguous. Practical setting…

cs.CL2021★ 26 cited

Robust Deep Reinforcement Learning for Extractive Legal Summarization

Duy-Hung Nguyen, Bao-Sinh Nguyen, Nguyen Viet Dung Nghiem +4

Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles. Recent…