方法对比
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| 自然语言生成× | 文本摘要× | |
|---|---|---|
| 领域 | 文本挖掘 | 文本挖掘 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 1970s (rule-based origins); 2000s (probabilistic); 2017+ (neural/transformer era) | — |
| 提出者≠ | Reiter & Dale (classical pipeline, 2000); Gatt & Krahmer (modern survey, 2018) | — |
| 类型≠ | NLP generative task — structured data to natural language | NLP text-generation / text-reduction task |
| 开创性文献≠ | Gatt, A. & Krahmer, E. (2018). Survey of the State of the Art in Natural Language Generation: Core Tasks, Applications and Evaluation. Journal of Artificial Intelligence Research, 61, 65-170. link ↗ | Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval. DOI ↗ |
| 别名 | NLG, data-to-text, text generation, Doğal Dil Üretimi (NLG) | automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme |
| 相关≠ | 7 | 4 |
| 摘要≠ | Natural Language Generation (NLG) is the branch of natural language processing that automatically produces fluent, human-readable text from structured data, knowledge graphs, or semantic representations. Formalised in the classical pipeline by Reiter and Dale (2000) and surveyed comprehensively by Gatt and Krahmer (2018), NLG powers applications ranging from automated financial reporting and weather bulletins to data storytelling and conversational agents. | Automatic text summarization is a natural-language-processing task that condenses long documents into shorter summaries while preserving their key information. It works through one of two families of approaches — extractive summarization, which selects the most important spans from the source, or abstractive summarization, which generates new text. The field was consolidated by Nenkova and McKeown (2011), and sequence-to-sequence models such as BART (Lewis et al., 2020) advanced the abstractive side. |
| ScholarGate数据集 ↗ |
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