no code implementations • EMNLP 2020 • Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, Shi Wang
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.
Ranked #1 on Extractive Text Summarization on CNN / Daily Mail
no code implementations • 6 Jan 2024 • Qian Li, Lixin Su, Jiashu Zhao, Long Xia, Hengyi Cai, Suqi Cheng, Hengzhu Tang, Junfeng Wang, Dawei Yin
Compared to conventional textual retrieval, the main obstacle for text-video retrieval is the semantic gap between the textual nature of queries and the visual richness of video content.
1 code implementation • 8 Jul 2021 • Jiangxia Cao, Xixun Lin, Xin Cong, Shu Guo, Hengzhu Tang, Tingwen Liu, Bin Wang
A temporal interaction network consists of a series of chronological interactions between users and items.
no code implementations • COLING 2020 • Zhenyu Zhang, Bowen Yu, Xiaobo Shu, Tingwen Liu, Hengzhu Tang, Wang Yubin, Li Guo
Document-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to reach the final result.
1 code implementation • 23 Jun 2020 • Xin Cong, Bowen Yu, Tingwen Liu, Shiyao Cui, Hengzhu Tang, Bin Wang
We first build a representation extractor to derive features for unlabeled data from the target domain (no test data is necessary) and then group them with a cluster miner.
no code implementations • 28 Mar 2020 • Hengzhu Tang, Yanan Cao, Zhen-Yu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, Pengfei Yin
In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.
Ranked #51 on Relation Extraction on DocRED