12 citations · 19 across the 6 of their papers we have counts for
6 papers
Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k
Zangwei Zheng, Xiangyu Peng, Yuxuan Lou +30
Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quant…
Open-Sora: Democratizing Efficient Video Production for All
Zangwei Zheng, Xiangyu Peng, Tianji Yang +6
Vision and language are the two foundational senses for humans, and they build up our cognitive ability and intelligence. While significant breakthroughs have been made in AI langu…
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen +2
With the recent undeniable advancement in reasoning abilities in large language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks. One…
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen +2
Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-d…
SentBS: Sentence-level Beam Search for Controllable Summarization
Chenhui Shen, Liying Cheng, Lidong Bing +2
A wide range of control perspectives have been explored in controllable text generation. Structure-controlled summarization is recently proposed as a useful and interesting researc…
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation
Chenhui Shen, Liying Cheng, Ran Zhou +3
When directly using existing text generation datasets for controllable generation, we are facing the problem of not having the domain knowledge and thus the aspects that could be c…