8 citations · 21 across the 18 of their papers we have counts for
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cs.CL2022★ 2 cited
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework based on Semantic Blocks
Xinnian Liang, Jing Li, Shuangzhi Wu +4
Unsupervised summarization methods have achieved remarkable results by incorporating representations from pre-trained language models. However, existing methods fail to consider ef…
cs.CL2022★ 1 cited
Modeling Paragraph-Level Vision-Language Semantic Alignment for Multi-Modal Summarization
Chenhao Cui, Xinnian Liang, Shuangzhi Wu +1
Most current multi-modal summarization methods follow a cascaded manner, where an off-the-shelf object detector is first used to extract visual features, then these features are fu…
cs.CL2022
Modeling Multi-Granularity Hierarchical Features for Relation Extraction
Xinnian Liang, Shuangzhi Wu, Mu Li +1
Relation extraction is a key task in Natural Language Processing (NLP), which aims to extract relations between entity pairs from given texts. Recently, relation extraction (RE) ha…