mlsanityData to 14 Oct 2026

Text-Blueprint: An Interactive Platform for Plan-based Conditional Generation

arXiv 2305.00034 ↗First posted 28 Apr 2023cs.CLcited by 9

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Abstract (arXiv)

While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We present a web browser-based demonstration for query-focused summarization that uses a sequence of question-answer pairs, as a blueprint plan for guiding text generation (i.e., what to say and in what order). We illustrate how users may interact with the generated text and associated plan visualizations, e.g., by editing and modifying the blueprint in order to improve or control the generated output. A short video demonstrating our system is available at https://goo.gle/text-blueprint-demo.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 28 Apr 2023.

Builds on

34 of 47 references.

References · 34

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  6. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  7. 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
  8. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 900
  9. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  10. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 584
  11. 2019Text Summarization with Pretrained Encoderscited by 556
  12. 2019GLTR: Statistical Detection and Visualization of Generated Textcited by 496
22 more
  1. 2020Towards a Human-like Open-Domain Chatbotcited by 470
  2. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 396
  3. 2021LongT5: Efficient Text-To-Text Transformer for Long Sequencescited by 232
  4. 2017ParlAI: A Dialog Research Software Platformcited by 139
  5. 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
  6. 2018Data-to-Text Generation with Content Selection and Planningcited by 106
  7. 2020CTRLsum: Towards Generic Controllable Text Summarizationcited by 98
  8. 2020GO FIGURE: A Meta Evaluation of Factuality in Summarizationcited by 76
  9. 2019Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generationcited by 75
  10. 2021Planning with Learned Entity Prompts for Abstractive Summarizationcited by 75
  11. 2022Conditional Generation with a Question-Answering Blueprintcited by 44
  12. 2020AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarizationcited by 42
  13. 2019Visual Interaction with Deep Learning Models through Collaborative Semantic Inferencecited by 24
  14. 2018Structure-Infused Copy Mechanisms for Abstractive Summarizationcited by 17
  15. 2018Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representationcited by 16
  16. 2021TextBox: A Unified, Modularized, and Extensible Framework for Text Generationcited by 15
  17. 2022Mapping the Design Space of Human-AI Interaction in Text Summarizationcited by 15
  18. 2018Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generationcited by 13
  19. 2021SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarizationcited by 13
  20. 2016Cutting-off Redundant Repeating Generations for Neural Abstractive Summarizationcited by 7
  21. 2022Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfercited by 7
  22. 2021Summary Explorer: Visualizing the State of the Art in Text Summarizationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 2; 2025: 4; 2026: 1.

9 papers.