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| Information Extraction× | Textzusammenfassung – Extraktiv und Abstraktiv× | |
|---|---|---|
| Fachgebiet | Text Mining | Text Mining |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr | — | — |
| Urheber | — | — |
| Typ≠ | NLP structured-information task | NLP text-generation / text-reduction task |
| Wegweisende Quelle≠ | Cowie, J. & Lehnert, W. (1996). Information Extraction. Communications of the ACM. DOI ↗ | Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval. DOI ↗ |
| Aliasnamen≠ | IE, structured information extraction, Bilgi Çıkarma (Information Extraction) | automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Information extraction (IE) is a natural-language-processing task that converts unstructured text into structured information — such as events, relations, and attributes — so that facts buried in free-form documents become machine-readable records. The task was consolidated in early surveys by Cowie and Lehnert (1996) and later by Grishman (2012). | 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. |
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