Papers with perplexity-based method
Source Identification in Abstractive Summarization (2024.eacl-short)
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| Challenge: | Existing studies define input sentences that contain essential information in the generated summary as source sentences. |
| Approach: | They define input sentences that contain essential information in the generated summary as source sentences and analyze the source sentences to determine how abstractive summaries are made. |
| Outcome: | The proposed method performs well in abstractive settings, while similarity-based methods perform robustly in extractive settings. |
Contextualized Semantic Distance between Highly Overlapped Texts (2023.findings-acl)
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| Challenge: | Conventional semantic metrics are based on word representations and are vulnerable to disturbance of overlapped components with similar representations. |
| Approach: | They propose a mask-and-predict strategy to evaluate the semantic distance between the overlapped sentences using words in the longest common sequence as neighboring words and use masked language modeling to predict their positions. |
| Outcome: | The proposed method outperforms the state-of-the-art in domain adaption by a huge margin. |