| Challenge: | Current methods for generating text are opaque and difficult to control and interpret due to their opaque nature. |
| Approach: | They propose a modular approach with separate components for each step . they formalize Fusion-in-Context as a standalone task, whose input consists of source texts with highlighted spans of targeted content. |
| Outcome: | The proposed approach is based on a curated dataset of 1000 instances in the reviews domain and a novel evaluation framework for assessing the faithfulness and coverage of highlights. |
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| Challenge: | Sentence fusion is the task of joining related sentences into coherent text. |
| Approach: | They propose a method where ground-truth solutions are automatically expanded into multiple references via curated equivalence classes of connective phrases. |
| Outcome: | The proposed approach improves on state-of-the-art models by expanding ground-truth solutions into multiple references. |
Controlled Text Reduction (2022.emnlp-main)
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| Challenge: | Abstractive text summarization models separate the salience detection phase from the text generation phase. |
| Approach: | They propose to formalize Controlled Text Reduction as a standalone task . they advocate the potential of such models for modular fully-automatic summarization . |
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A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion (2020.aacl-main)
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| Challenge: | Existing systems that perform content selection and surface realization are not able to provide sufficient training data for news summarization. |
| Approach: | They propose to use a cascade architecture to perform content selection and surface realization together to generate abstracts. |
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Controlling the Focus of Pretrained Language Generation Models (2022.findings-acl)
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| Challenge: | Existing mechanisms to control the model's focus are not available for pretrained transformer-based language generation models. |
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Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion (2026.acl-long)
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| Challenge: | Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information. |
| Approach: | They propose a two-stage automated pipeline that uses pretrained models to extract contextual cues from video . a large language model synthesizes these inputs to generate detailed and context-aware captions . |
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PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation (2022.acl-long)
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| Challenge: | Existing methods for text generation still suffer from incoherence problems . Neural sequence-to-sequence (seq2sequ) models generate fluent results . |
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Model Criticism for Long-Form Text Generation (2022.emnlp-main)
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| Challenge: | Language models generate fluent text, but it remains unclear whether output retains coherent high-level structure. |
| Approach: | They propose to use a statistical tool to evaluate high-level structure of text . they compare distributions between real and generated data in latent space . |
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Attribute First, then Generate: Locally-attributable Grounded Text Generation (2024.acl-long)
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| Challenge: | Recent efforts to address hallucinations in Large Language Models have focused on attributed text generation, which supplements generated texts with citations of supporting sources for post-generation fact-checking and corrections. |
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A Sentiment Consolidation Framework for Meta-Review Generation (2024.acl-long)
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| Approach: | They propose to prompt large language models to generate meta-reviews and use evaluation metrics to assess the quality of generated meta- reviews. |
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Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)
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| Challenge: | Existing models focus on local word prediction, and cannot make high level plans on what to generate. |
| Approach: | They propose a pipelined system that summarises, outlines and elaborates on each bullet point to generate the corresponding segment. |
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