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20122022
most citedA Survey of Point-of-interest Recommendation in Location-based Social Networks

79 citations · 583 across the 45 of their papers we have counts for

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30 papers · 1 filter

cs.CL2022

Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction

Xuming Hu, Shiao Meng, Chenwei Zhang +4

Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NE…

cs.CL20222 cited

Text Revision by On-the-Fly Representation Optimization

Jingjing Li, Zichao Li, Tao Ge +2

Text revision refers to a family of natural language generation tasks, where the source and target sequences share moderate resemblance in surface form but differentiate in attribu…

cs.CL202121 cited

Towards Efficient Post-training Quantization of Pre-trained Language Models

Haoli Bai, Lu Hou, Lifeng Shang +3

Network quantization has gained increasing attention with the rapid growth of large pre-trained language models~(PLMs). However, most existing quantization methods for PLMs follow…

cs.CL2021

Controllable Summarization with Constrained Markov Decision Process

Hou Pong Chan, Lu Wang, Irwin King

We study controllable text summarization which allows users to gain control on a particular attribute (e.g., length limit) of the generated summaries. In this work, we propose a no…

cs.CL2021

Dialogue Summarization with Supporting Utterance Flow Modeling and Fact Regularization

Wang Chen, Piji Li, Hou Pong Chan +1

Dialogue summarization aims to generate a summary that indicates the key points of a given dialogue. In this work, we propose an end-to-end neural model for dialogue summarization…

cs.CL20211 cited

A Training-free and Reference-free Summarization Evaluation Metric via Centrality-weighted Relevance and Self-referenced Redundancy

Wang Chen, Piji Li, Irwin King

In recent years, reference-based and supervised summarization evaluation metrics have been widely explored. However, collecting human-annotated references and ratings are costly an…