28 citations · 92 across the 13 of their papers we have counts for
6 papers · 1 filter
FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness
Wenhao Wu, Wei Li, Jiachen Liu +4
Despite being able to generate fluent and grammatical text, current Seq2Seq summarization models still suffering from the unfaithful generation problem. In this paper, we study the…
Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation
Wenhao Wu, Wei Li, Jiachen Liu +3
Though model robustness has been extensively studied in language understanding, the robustness of Seq2Seq generation remains understudied. In this paper, we conduct the first quant…
Faithfulness in Natural Language Generation: A Systematic Survey of Analysis, Evaluation and Optimization Methods
Wei Li, Wenhao Wu, Moye Chen +3
Natural Language Generation (NLG) has made great progress in recent years due to the development of deep learning techniques such as pre-trained language models. This advancement h…
SgSum: Transforming Multi-document Summarization into Sub-graph Selection
Moye Chen, Wei Li, Jiachen Liu +3
Most of existing extractive multi-document summarization (MDS) methods score each sentence individually and extract salient sentences one by one to compose a summary, which have tw…
BASS: Boosting Abstractive Summarization with Unified Semantic Graph
Wenhao Wu, Wei Li, Xinyan Xiao +5
Abstractive summarization for long-document or multi-document remains challenging for the Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text.…
Leveraging Graph to Improve Abstractive Multi-Document Summarization
Wei Li, Xinyan Xiao, Jiachen Liu +3
Graphs that capture relations between textual units have great benefits for detecting salient information from multiple documents and generating overall coherent summaries. In this…