Papers by Yueguan Wang

2 papers
Are All Steps Equally Important? Benchmarking Essentiality Detection in Event Processes (2023.emnlp-main)

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Challenge: Existing models of event processing do not understand the essentiality of step events towards a goal event.
Approach: They propose to deconstruct a goal event into a discrete representation of finer-grained (step) events, which are not equally important to the goal.
Outcome: The proposed model can understand the essentiality of different step events towards a goal event.
Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation (2023.acl-long)

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Challenge: Existing work on cross-lingual summarization (CLS) does not consider crosslingual sources for summarizing.
Approach: They propose a cross-lingual conversation summarization benchmark that explicitly considers source context.
Outcome: The proposed method surpasses baselines on ConvSumX and 3 widely-used manual annotations.

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