Papers with Controlled Text Reduction
Controlled Text Reduction (2022.emnlp-main)
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| Challenge: | Abstractive text summarization models separate the salience detection phase from the text generation phase. |
| Approach: | They propose to formalize Controlled Text Reduction as a standalone task . they advocate the potential of such models for modular fully-automatic summarization . |
| Outcome: | The proposed model shows that it is possible to produce a reduced version of a source text using decomposed modeling. |
Don’t Add, don’t Miss: Effective Content Preserving Generation from Pre-Selected Text Spans (2023.findings-emnlp)
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| Challenge: | Existing CTR models are mediocre and lack reliable performance . authors propose an explicit decomposition of these two subtasks into a single task . |
| Approach: | They propose an isolated task that challenges models to generate coherent text conforming to pre-selected content within the input text ("highlights") authors propose a high-quality, open-source CTR model that tackles two prior key limitations: inadequate enforcement of the content-preservation constraint, and suboptimal silver training data. |
| Outcome: | The proposed model significantly improves silver training data quality over the existing model, with up to 30 ROUGE-L points. |