| Challenge: | Existing methods for topic-to-essay generation are insufficient for generating novel, diverse, and topic-consistent paragraph-level text with a set of topics. |
| Approach: | They propose to integrate commonsense from external knowledge base into the generator through dynamic memory mechanism and adversarial training to further improve topic-consistency. |
| Outcome: | The proposed task is more novel, diverse, and topic-consistent than existing methods in terms of both automatic and human evaluation. |
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| Challenge: | Existing methods for commonsense question generation produce shallow questions that can be answered by simple word matching. |
| Approach: | They propose a task of commonsense question generation that aims to yield deep-level questions from the text. |
| Outcome: | The proposed model can yield deep-level and to-the-point questions from the text. |
AEG: Argumentative Essay Generation via A Dual-Decoder Model with Content Planning (2022.emnlp-main)
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| Challenge: | Existing studies on argument generation focus on generating individual short arguments, while research on generating long and coherent argumentative essays is under-explored. |
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Elaboration-Generating Commonsense Question Answering at Scale (2023.acl-long)
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| Challenge: | elaborations are generated using language models that generate background knowledge that helps improve performance . human evaluations show that the quality of the generated ellaborations is high . |
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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. |
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Enhancing Neural Data-To-Text Generation Models with External Background Knowledge (D19-1)
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| Challenge: | Recent neural models for data-to-text generation rely on parallel pairs of data and text to learn writing knowledge. |
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TWAG: A Topic-Guided Wikipedia Abstract Generator (2021.acl-long)
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| Challenge: | Existing models view Wikipedia abstract as plain text, ignoring that it is a description of a certain entity and can be decomposed into different topics. |
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A Knowledge-Enhanced Pretraining Model for Commonsense Story Generation (2020.tacl-1)
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| Challenge: | Existing models for story generation suffer from repetition, logic conflicts, and lack of long-range coherence . |
| Approach: | They propose to utilize commonsense knowledge from external knowledge bases to generate reasonable stories by multi-task learning. |
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Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation (2024.emnlp-main)
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An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
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CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning (2020.findings-emnlp)
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| Challenge: | Recent studies show that pre-trained language models perform well on commonsense-reasoning benchmark datasets, but building machines with commonsence to compose plausible sentences remains challenging. |
| Approach: | They propose a constrained text generation task for generative commonsense reasoning that generates a coherent sentence using common concepts. |
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