activity
20192021
most citedNeural Related Work Summarization with a Joint Context-driven Attention Mechanism

14 citations · 33 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CY2021★ 2 cited

Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy

Yongzhen Wang, Xiaozhong Liu, Katy Börner +4

Objective: Ubiquitous internet access is reshaping the way we live, but it is accompanied by unprecedented challenges in preventing chronic diseases that are usually planted by lon…

cs.CL2020★ 1 cited

Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases

Sheng Bi, Xiya Cheng, Yuan-Fang Li +2

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of (connected) triples. Two main challenges still face th…

cs.CL2020★ 2 cited

Knowledge-aware Method for Confusing Charge Prediction

Xiya Cheng, Sheng Bi, Guilin Qi +1

Automatic charge prediction task aims to determine the final charges based on fact descriptions of criminal cases, which is a vital application of legal assistant systems. Conventi…

cs.IR2020★ 10 cited

Community-Based Data Integration of Course and Job Data in Support of Personalized Career-Education Recommendations

Guoqing Zhu, Naga Anjaneyulu Kopalle, Yongzhen Wang +3

How does your education impact your professional career? Ideally, the courses you take help you identify, get hired for, and perform the job you always wanted. However, not all cou…

cs.CY2020★ 4 cited

Chinese E-Romance: Analyzing and Visualizing 7.92 Million Alibaba Valentine's Day Purchases

Yongzhen Wang, Xiaozhong Liu, Yingnan Ju +4

The days that precede Valentine's Day are characterized by extensive gift shopping activities all across the globe. In China, where much shopping takes place online, there has been…

cs.CL2019★ 14 cited

Neural Related Work Summarization with a Joint Context-driven Attention Mechanism

Yongzhen Wang, Xiaozhong Liu, Zheng Gao

Conventional solutions to automatic related work summarization rely heavily on human-engineered features. In this paper, we develop a neural data-driven summarizer by leveraging th…