Papers by Yutong Qu

1 papers
Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)

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Challenge: Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult.
Approach: They propose an unsupervised method that leverages contrastive learning to generate summaries by rewriting and paraphrasing the source documents to generate good summary.
Outcome: The proposed method outperforms baseline methods on extensive experiments on source documents and fake documents.

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