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Wang Chen
Researcher at The Chinese University of Hong Kong
Publications - 18
Citations - 408
Wang Chen is an academic researcher from The Chinese University of Hong Kong. The author has contributed to research in topics: Automatic summarization & Computer science. The author has an hindex of 7, co-authored 16 publications receiving 241 citations.
Papers
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Journal ArticleDOI
Title-Guided Encoding for Keyphrase Generation
TL;DR: Li et al. as discussed by the authors introduced a new model called title-guided network (TG-Net) for automatic keyphrase generation task based on the encoderdecoder architecture with two new features: (i) the title is additionally employed as a query-like input, and (ii) a titleguided encoder gathers the relevant information from the title to each word in the document.
Proceedings ArticleDOI
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards.
TL;DR: This paper proposed a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases, and introduced a new evaluation method that incorporates name variations of the ground-truth key-phrase using the Wikipedia knowledge base.
Proceedings ArticleDOI
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction.
TL;DR: The authors proposed a multi-task learning framework that jointly learns an extractive model and a generative model for keyphrase generation, which can better identify important contents from the given document.
Proceedings ArticleDOI
Difficulty Controllable Generation of Reading Comprehension Questions
TL;DR: The results show that the question generated by the end-to-end framework to generate questions of designated difficulty levels not only have better quality under the metrics like BLEU, but also comply with the specified difficulty labels.
Proceedings ArticleDOI
Exclusive Hierarchical Decoding for Deep Keyphrase Generation
TL;DR: This article propose an exclusive hierarchical decoding framework that includes a hierarchical decoding process and either a soft or a hard exclusion mechanism to explicitly model the hierarchical compositionality of a keyphrase set, which can enhance the diversity of the generated keyphrases.