| Challenge: | Text-to-text generation tasks require copying words from the input to the output. |
| Approach: | They propose a transformer-based pointer network for text-to-text generation which generates more abstractive summaries and a further extension of this architecture for automatic post-editing. |
| Outcome: | The proposed model outperforms existing models in text-to-text generation tasks and improves translation accuracy. |
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| Challenge: | Existing interactive writing assistants do not allow authors to guide text generation in desired topical directions. |
| Approach: | They propose a framework that displays multiple candidate upcoming topics and generates a text generation model that adheres to the chosen topics. |
| Outcome: | The proposed model generates fluent sentences related to the selected topics, as judged by automated metrics and crowdsourced workers. |
Data-to-text Generation by Splicing Together Nearest Neighbors (2021.emnlp-main)
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| Challenge: | Existing work on data-to-text generation relies on retrieved "neighbors" but instead generates text token-by-token, left-to right. |
| Approach: | They propose to splice together retrieved segments of text from "neighbor" source-target pairs to generate text token-by-token, left-to-right. |
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Online Back-Parsing for AMR-to-Text Generation (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) is a semantic graph representation that abstracts meaning away from a sentence. |
| Approach: | They propose a decoder that back predicts projected AMR graphs on target sentences . their results show superiority over previous state-of-the-art decoded graph Transformer . |
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Enhanced Transformer Model for Data-to-Text Generation (D19-56)
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| Challenge: | Neural models have shown significant progress on data-to-text generation tasks . data- to-text models generate descriptive texts from non-linguistic structured data . |
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On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
| Approach: | They propose a method to generate abstractive summaries of documents that exceed several thousand words via neural abstractive summary. |
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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. |
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Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach (2024.findings-eacl)
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| Challenge: | Large pre-trained language models such as GPT-3.5 and GPT-4 have gained significant attention in natural language research due to limited computational resources or inaccessible parameters. |
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Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)
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| Challenge: | Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document. |
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ReadOnce Transformers: Reusable Representations of Text for Transformers (2021.acl-long)
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| Challenge: | ReadOnce Transformers is a task-independent, task-dependent, and compressed representation of text. |
| Approach: | They propose a transformer-based model that can build an information-capturing, task-independent, and compressed representation of text. |
| Outcome: | The proposed model can build an information-capturing, task-independent, and compressed representation of text. |
Towards Table-to-Text Generation with Numerical Reasoning (2021.acl-long)
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| Challenge: | Recent studies have shown improvement in generating descriptive text from structured data. |
| Approach: | They propose a framework for numerical table-to-text generation based on numerical reasoning . they use a pre-trained model and a copy mechanism to fine-tune the models to produce fluent text . |
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