| Challenge: | Existing methods for story generation struggle with staying coherent for long periods of time. |
| Approach: | They propose a controlled generation task which expands a sequence of facts into a longer narrative. |
| Outcome: | The proposed model produces competitive fluency while adhering to the requested facts. |
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| Challenge: | Large pre-trained language models have enabled open-ended generation frameworks to tackle a variety of tasks beyond data-to-text generation. |
| Approach: | They propose a new task to generate a factual description about an entity given guiding keys and grounding passages using a dataset. |
| Outcome: | The proposed model improves factual correctness and recall significantly compared to previous models. |
Text-to-Text Automatic Story Generation: A Survey (2026.eacl-srw)
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| Challenge: | Automated story generation aims to produce coherent, engaging, and contextually consistent narratives with minimal or no human involvement . despite advances in large language models, maintaining narrative coherence, character consistency, storyline diversity, and plot controllability in generating stories is still challenging. |
| Approach: | They propose to develop new evaluation metrics and better data sets to support automatic story generation. |
| Outcome: | The proposed evaluation metrics and better datasets will improve narrative coherence and consistency and explore practical applications of story generation. |
Towards Informative Open-ended Text Generation with Dynamic Knowledge Triples (2023.findings-emnlp)
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| Challenge: | Pretrained language models (PLMs) have impressive capabilities in open-ended text generation. |
| Approach: | They propose a dynamic knowledge-guided informative open-ended text generation approach that utilizes a knowledge graph to help the model generate more contextually related entities and detailed facts. |
| Outcome: | The proposed approach generates more informative texts than baselines. |
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. |
| Approach: | They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles. |
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Knowledge-Enriched Natural Language Generation (2021.emnlp-tutorials)
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| Challenge: | Knowledge-enriched text generation poses unique challenges in modeling and learning . a roadmap will outline the state-of-the-art methods to tackle these challenges . |
| Approach: | They propose a roadmap to tackle the challenges of knowledge-enriched text generation . they will dive deep into various technical components to illustrate how to represent knowledge . |
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Progressive Generation of Long Text with Pretrained Language Models (2021.naacl-main)
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| Challenge: | Existing methods for "long" text generation are limited to outputs of 50-200 tokens . however, our proposed ProGen generates coherent long passages of text in a progressive manner . |
| Approach: | They propose a method for generating coherent long passages of text in a progressive manner . they first produce domain-specific content keywords and then refine them into complete passages . human evaluation validates that their proposed generation is more coherent . |
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MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models (2020.emnlp-main)
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Peng Xu, Mostofa Patwary, Mohammad Shoeybi, Raul Puri, Pascale Fung, Anima Anandkumar, Bryan Catanzaro
| Challenge: | Existing pre-trained large language models have shown unparalleled generative capabilities, but they are not controllable. |
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Sentence Smith: Controllable Edits for Evaluating Text Embeddings (2025.emnlp-main)
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| Challenge: | Controllable and transparent text generation has been a long-standing goal in NLP . but previous approaches were hindered by parsing and generation insufficiencies . |
| Approach: | They propose a framework for English that has three steps: 1. Parsing a sentence into a semantic graph. 2. Applying human-designed semantic manipulation rules. 3. Generating text from the manipulated graph. |
| Outcome: | The proposed framework for English is based on a neural network and parsers. |
Controllable Meaning Representation to Text Generation: Linearization and Data Augmentation Strategies (2020.emnlp-main)
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| Challenge: | Using task-oriented dialogue generation benchmarks, we compare the effect of four input linearization strategies on controllability and faithfulness. |
| Approach: | They compare the effect of four input linearization strategies on controllability and faithfulness . they also evaluate how a phrase-based data augmentation method can improve performance . |
| Outcome: | The proposed model can generate utterances whose phrases follow the order of the provided plan. |
Expository Text Generation: Imitate, Retrieve, Paraphrase (2023.emnlp-main)
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| Challenge: | Expository documents are vital resources for conveying complex information to readers. |
| Approach: | They propose a task to generate an accurate and stylistically consistent expository text by intelligently searching a knowledge source. |
| Outcome: | The proposed framework overcomes the limitations of retrieval-augmented models and produces factual and organized expository texts that accurately inform readers. |