Papers with unsupervised algorithm
Unsupervised Semantic Abstractive Summarization (P18-3)
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| Challenge: | Existing methods for abstractive summarization are limited in the sense that they can never generate human level summaries for large and complicated documents. |
| Approach: | They propose a pipeline for automatic abstractive summary generation using co-reference resolution and Meta Nodes. |
| Outcome: | The proposed pipeline outperforms the state-of-the-art method by 1.7% in node prediction. |
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models (2021.acl-long)
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| Challenge: | Existing methods for generating text are unsupervised and require supervision. |
| Approach: | They propose an unsupervised method that uses two off-the-shelf pretrained LMs in opposite directions to apply them to non-sequential tasks. |
| Outcome: | The proposed method outperforms strong unsupervised baselines on paraphrasing and abductive text infilling. |
An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification (2020.coling-main)
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| Challenge: | Existing methods for learning multi-word expressions have language sparsity and are not supervised. |
| Approach: | They propose an unsupervised approach to learning a compositional representation function for multi-word expressions . they use a Tratz dataset to train the composition function on the word-semantic relation . |
| Outcome: | The proposed method outperforms the previous state-of-the-art method on the Tratz dataset with an F1 score of 50.4%. |
Hierarchical Trivia Fact Extraction from Wikipedia Articles (2020.coling-main)
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| Challenge: | Existing methods for extracting trivia facts for Wikipedia categories are not efficient . a trivia fact is an interesting fact that is unusual, unexpected, or unique . |
| Approach: | They propose an unsupervised algorithm that automatically mines trivia facts for a given entity . they propose to target at a single Wikipedia article and leverage its hierarchical structure . |
| Outcome: | The proposed algorithm outperforms existing methods and is 100 times faster than existing methods. |