Papers with unsupervised multi-document summarization
SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization (2020.acl-main)
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| Challenge: | Existing evaluation methods for document summarization require human annotations and annotations. |
| Approach: | They propose a method which measures the quality of a summary by measuring its semantic similarity with a pseudo reference summary, using contextualized embeddings and soft token alignment techniques. |
| Outcome: | The proposed method correlates better with human ratings by 18- 39% compared to the state-of-the-art evaluation metrics. |