FASST, Few-Shot Abstractive Summarization for Style Transfer
The paper introduces a novel unsupervised text style transfer method using few-shot abstractive summarization. This approach aligns vector space embeddings of source and target texts, selects nearest neighbors based on semantic similarity, and reranks the summarization. This method significantly improves style transfer quality and achieves soa results in automatic evaluation metrics.
Sep 1, 2023
