PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation (2022.acl-long)
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| Challenge: | Existing methods for text generation still suffer from incoherence problems . Neural sequence-to-sequence (seq2sequ) models generate fluent results . |
| Approach: | They propose a novel generation framework that leverages autoregressive self-attention mechanism to conduct content planning and surface realization dynamically. |
| Outcome: | The proposed framework outperforms baseline models and generates more coherent texts with richer contents. |
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