Facts2Story: Controlling Text Generation by Key Facts

Orbach, Eyal; Goldberg, Yoav

doi:10.18653/v1/2020.coling-main.211

“…Wikipedia has also been used to construct datasets for other text generation tasks, such as generating Wikipedia movie plots (Orbach and Goldberg, 2020;Rashkin et al, 2020) and short Wikipedia event summaries (Gholipour Ghalandari et al, 2020), and summarizing Wikipedia documents (Zopf, 2018; or summaries of aspects of interests (Hayashi et al, 2020) from relevant documents.…”

Section: Related Workmentioning

confidence: 99%

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

Chen

¹

,

Wiseman

²

,

Gimpel

³

2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Datasets for data-to-text generation typically focus either on multi-domain, single-sentence generation or on single-domain, long-form generation. In this work, we cast generating Wikipedia sections as a data-to-text generation task and create a large-scale dataset, WIKITABLET, that pairs Wikipedia sections with their corresponding tabular data and various metadata. WIKITABLET contains millions of instances, covering a broad range of topics, as well as a variety of flavors of generation tasks with different levels of flexibility. We benchmark several training and decoding strategies on WIKITABLET. Our qualitative analysis shows that the best approaches can generate fluent and high quality texts but they struggle with coherence and factuality, showing the potential for our dataset to inspire future work on long-form generation. 1

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“…To alleviate this problem, one research direction adopts coarse-to-fine progressive text generation (Tan et al, 2021). This generation paradigm has been studied in many text generation systems for specific tasks, such as datato-text generation (Moryossef et al, 2019;Puduppully and Lapata, 2021), storytelling (Goldfarb-Tarrant et al, 2020;Orbach and Goldberg, 2020), and dialogue generation (Xu et al, 2020a). Our work adopts a generative event transition planner that is trained on a large amount of event transition paths, aiming to arrange the ensuing events in open-ended text generation.…”

Section: Related Workmentioning

confidence: 99%

Event Transition Planning for Open-ended Text Generation

Li¹,

Li²,

Bi³

et al. 2022

Findings of the Association for Computational Linguistics: ACL 2022

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Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context. The openended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays. Despite these neural models are good at producing human-like text, it is difficult for them to arrange causalities and relations between given facts and possible ensuing events. To bridge this gap, we propose a novel two-stage method which explicitly arranges the ensuing events in open-ended text generation. Our approach can be understood as a specially-trained coarse-to-fine algorithm, where an event transition planner provides a "coarse" plot skeleton and a text generator in the second stage refines the skeleton. Experiments on two open-ended text generation tasks demonstrate that our proposed method effectively improves the quality of the generated text, especially in coherence and diversity. The code is available at: https://github.com/ qtli/EventPlanforTextGen.

show abstract

“…To alleviate this problem, one research direction adopts coarse-to-fine progressive text generation (Tan et al, 2021). This generation paradigm has been studied in many text generation systems for specific tasks, such as datato-text generation (Moryossef et al, 2019;Puduppully and Lapata, 2021), storytelling (Goldfarb-Tarrant et al, 2020;Orbach and Goldberg, 2020), and dialogue generation (Xu et al, 2020a). Our work adopts a generative event transition planner that is trained on a large amount of event transition paths, aiming to arrange the ensuing events in open-ended text generation.…”

Section: Related Workmentioning

confidence: 99%

Event Transition Planning for Open-ended Text Generation

Li¹,

Li²,

Bi³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context. The openended nature of these tasks brings new challenges to the neural auto-regressive text generators nowadays. Despite these neural models are good at producing human-like text, it is difficult for them to arrange causalities and relations between given facts and possible ensuing events. To bridge this gap, we propose a novel two-stage method which explicitly arranges the ensuing events in open-ended text generation. Our approach can be understood as a specially-trained coarse-to-fine algorithm, where an event transition planner provides a "coarse" plot skeleton and a text generator in the second stage refines the skeleton. Experiments on two open-ended text generation tasks demonstrate that our proposed method effectively improves the quality of the generated text, especially in coherence and diversity. The code is available at: https://github.com/ qtli/EventPlanforTextGen.

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Facts2Story: Controlling Text Generation by Key Facts

Cited by 10 publications

References 19 publications

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections

Event Transition Planning for Open-ended Text Generation

Event Transition Planning for Open-ended Text Generation

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