Papers with single-document extractive summarization
Deep Differential Amplifier for Extractive Summarization (2021.acl-long)
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| Challenge: | Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance. |
| Approach: | They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
Single Document Summarization as Tree Induction (N19-1)
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| Challenge: | Existing approaches to extractive summarization use recurrent neural networks to model document . Existing systems use a vector representation for each sentence to generate a summary . |
| Approach: | They propose a model that induces a multi-root dependency tree while predicting the output summary. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
BanditSum: Extractive Summarization as a Contextual Bandit (D18-1)
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| Challenge: | Existing methods for extractive summarization are heuristically generated and require a set of binary labels to be selected. |
| Approach: | They propose a method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. |
| Outcome: | The proposed method achieves better ROUGE scores than the state-of-the-art methods and significantly fewer update steps than competing approaches. |