Challenge: Modern neural generation systems conflate these two steps into a single end-to-end differentiable system.
Approach: They propose to split the generation process into a symbolic text-planning stage that is faithful to the input, followed by a neural generation stage that focuses only on realization.
Outcome: The proposed method improves reliability and adequacy while maintaining fluent output.

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Data-to-text Generation with Macro Planning (2021.tacl-1)

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Challenge: Recent approaches to data-to-text generation adopt the encoder-decoder architecture . however, these models perform poorly at selecting appropriate content and ordering it coherently .
Approach: They propose a neural model with a macro planning stage followed by a generation stage . they use data from databases of records, simulations of physical systems, accounting spreadsheets .
Outcome: The proposed model outperforms baselines on two data-to-text benchmarks . it uses the encoderdecoder architecture and is compared with existing models .
Plan-then-Generate: Controlled Data-to-Text Generation via Planning (2021.findings-emnlp)

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Challenge: Existing studies focus on producing results that are close to the references, i.e. what to generate and in what order (the output structure) cannot be explicitly controlled by the users.
Approach: They propose a Plan-then-Generate framework to improve the controllability of neural data-to-text models.
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Data-to-text Generation with Variational Sequential Planning (2022.tacl-1)

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Challenge: Recent advances in data-to-text generation have greatly facilitated the task of generating textual output from non-linguistic input.
Approach: They propose a neural model enhanced with a planning component responsible for organizing high-level information in a coherent and meaningful way.
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Enhancing Content Planning for Table-to-Text Generation with Data Understanding and Verification (2020.findings-emnlp)

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Challenge: Table-to-text models that select and order salient data and verbalize them fluently are lacking in content planning stage.
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Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)

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Challenge: Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization.
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Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity (2020.coling-main)

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Challenge: End-to-end neural data-totext generation has faced challenges generalizing to new domains and generating semantically consistent text.
Approach: They propose a neural data-to-text generation system that makes minimal assumptions about the data representation and target domain.
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Learning to Select, Track, and Generate for Data-to-Text (P19-1)

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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.
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Learning to Plan and Realize Separately for Open-Ended Dialogue Systems (2020.findings-emnlp)

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Challenge: Existing approaches to natural language generation are construed as end-to-end systems . however, some issues persist, such as coherence of output and repetition/hallucination of tokens .
Approach: They propose to decouple natural language generation into two phases: planning and realization.
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Plan-then-Seam: Towards Efficient Table-to-Text Generation (2023.findings-eacl)

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Challenge: Recent work explicitly decomposes the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively.
Approach: They propose a non-parallelelizable table-to-text model that produces outputs in parallel with one network.
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Operation-guided Neural Networks for High Fidelity Data-To-Text Generation (D18-1)

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Challenge: Recent neural models for data-to-text generation generate descriptions that are not consistent with structured data.
Approach: They propose a framework for data-to-text generation that uses symbolic operations to generate texts from structured data.
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