Papers by Liyuan Mao

2 papers
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)

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Challenge: lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations.
Approach: They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries.
Outcome: The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis.
Why Can Distillation Work with Limited Resources? A Systematic Study (2026.findings-acl)

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Challenge: Recent advances in large language models have driven reasoning performance . low-resource distillation can boost models' performance, but a framework is missing .
Approach: They conduct a controlled experiment to find out why low-resource distillation can boost model performance . they find that distillation enhances the presence of advanced cognitive behaviors .
Outcome: The proposed model shows more flexible reasoning, the authors show . they show that distillation enhances the presence of advanced cognitive behaviors .

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