Papers by Kunyuan Pang

2 papers
Multi-Document Scientific Summarization from a Knowledge Graph-Centric View (2022.coling-1)

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Challenge: Multi-Document Scientific Summarization (MDSS) aims to produce concise and concise summaries for clusters of topic-relevant scientific papers.
Approach: They propose a model that incorporates knowledge graphs into paper encoding and decoding processes and propose 'decoder' for generating knowledge graph information of summary in the form of descriptive sentences.
Outcome: The proposed architecture improves on baselines on the Multi-Xscience dataset.
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)

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Challenge: Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias.
Approach: They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator .
Outcome: The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters.

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