Papers with content planning
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. |
Learning to Select, Track, and Generate for Data-to-Text (P19-1)
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, Hiroya Takamura
| Challenge: | Existing models often refer to the same data record multiple times. |
| Approach: | They propose a data-to-text generation model with two modules, one for tracking and the other for text generation. |
| Outcome: | The proposed model outperforms existing models even without writer information in all evaluation metrics and contributes to content planning and surface realization. |
DYPLOC: Dynamic Planning of Content Using Mixed Language Models for Text Generation (2021.acl-long)
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| Challenge: | Existing neural generation models fall short of coherence, thus requiring efficient content planning. |
| Approach: | They propose a generation framework that conducts dynamic planning of content while generating the output based on a novel design of mixed language models. |
| Outcome: | The proposed model outperforms competing models on argument generation and writing articles using New York Times’ Opinion section. |
KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling (2025.emnlp-main)
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| Challenge: | Existing approaches to multi-hop question answering focus on generating simple questions and neglecting the integration of essential knowledge, such as relevant sentences within documents. |
| Approach: | They propose a framework to expand the diversity of generated multi-hop questions by sampling varied knowledge compositions within a given context. |
| Outcome: | The proposed framework improves the overall accuracy of knowledge composition selection by 3.9% on hotpotQA and 2WikiMultihopQA datasets. |