# Advancing Precise Outline-Conditioned Text Generation with Task Duality and Explicit Outline Control arXiv: https://arxiv.org/abs/2305.14459 ยท HTML: https://mlsanity.com/paper/2305.14459/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 48 references that are in the map. - First posted on arXiv 23 May 2023. ## Builds on (most cited first, 30 of 30) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019) - [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022) - [Hierarchical Neural Story Generation](https://mlsanity.com/paper/1805.04833/) (2018) - [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015) - [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015) - [Pointer Networks](https://mlsanity.com/paper/1506.03134/) (2015) - [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016) - [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019) - [FUDGE: Controlled Text Generation With Future Discriminators](https://mlsanity.com/paper/2104.05218/) (2021) - [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018) - [Re3: Generating Longer Stories With Recursive Reprompting and Revision](https://mlsanity.com/paper/2210.06774/) (2022) - [Strategies for Structuring Story Generation](https://mlsanity.com/paper/1902.01109/) (2019) - [Investigating Pretrained Language Models for Graph-to-Text Generation](https://mlsanity.com/paper/2007.08426/) (2020) - [PlotMachines: Outline-Conditioned Generation with Dynamic Plot State Tracking](https://mlsanity.com/paper/2004.14967/) (2020) - [Content Planning for Neural Story Generation with Aristotelian Rescoring](https://mlsanity.com/paper/2009.09870/) (2020) - [A Probabilistic Formulation of Unsupervised Text Style Transfer](https://mlsanity.com/paper/2002.03912/) (2020) - [Long and Diverse Text Generation with Planning-based Hierarchical Variational Model](https://mlsanity.com/paper/1908.06605/) (2019) - [Code Generation as a Dual Task of Code Summarization](https://mlsanity.com/paper/1910.05923/) (2019) - [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://mlsanity.com/paper/2108.13740/) (2021) - [Controllable Neural Story Plot Generation via Reward Shaping](https://mlsanity.com/paper/1809.10736/) (2018) - [Topic-Guided Variational Autoencoders for Text Generation](https://mlsanity.com/paper/1903.07137/) (2019) - [A Skeleton-Based Model for Promoting Coherence Among Sentences in Narrative Story Generation](https://mlsanity.com/paper/1808.06945/) (2018) - [DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation](https://mlsanity.com/paper/2210.09551/) (2022) - [Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries](https://mlsanity.com/paper/2010.07074/) (2020) - [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021) - [Progressive Generation of Long Text with Pretrained Language Models](https://mlsanity.com/paper/2006.15720/) (2020) - [Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events](https://mlsanity.com/paper/2101.00822/) (2021) ## Built on by (most cited first, 5 of 5) - [Large Concept Models: Language Modeling in a Sentence Representation Space](https://mlsanity.com/paper/2412.08821/) (2024) - [RAPID: Efficient Retrieval-Augmented Long Text Generation with Writing Planning and Information Discovery](https://mlsanity.com/paper/2503.00751/) (2025) - [Pap2Pat: Benchmarking Outline-Guided Long-Text Patent Generation with Patent-Paper Pairs](https://mlsanity.com/paper/2410.07009/) (2024) - [Segment-Level Diffusion: A Framework for Controllable Long-Form Generation with Diffusion Language Models](https://mlsanity.com/paper/2412.11333/) (2024) - [Improving Scientific Document Retrieval with Academic Concept Index](https://mlsanity.com/paper/2601.00567/) (2026) ## Abstract (arXiv) Existing works on outline-conditioned text generation typically aim to generate text using provided outlines as rough sketches, such as keywords and phrases. However, these approaches make it challenging to control the quality of text generation and assess consistency between outlines and generated texts due to lack of clarity and rationality of the rough outlines. In this paper, we introduce a novel text generation task called Precise Outline-conditioned Generation, which requires generating stories based on specific, sentence-level outlines. To facilitate research on this task, we construct two new datasets, WPOG and CDM. We provide strong baselines based on fine-tuning models such as BART and GPT-2, and evaluating zero-shot performance of models such as ChatGPT and Vicuna. Furthermore, we identify an issue of imbalanced utilization of the outline information in the precise outline-conditioned generation, which is ubiquitously observed across fine-tuned models and zero-shot inference models. To address this issue, we propose an explicit outline utilization control approach and a novel framework that leverages the task duality between summarization and generation. Experimental results show that the proposed approaches effectively alleviate the issue of imbalanced outline utilization and enhance the quality of precise outline-conditioned text generation for both fine-tuning and zero-shot settings.