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| Peringkasan Teks× | Kesamaan Semantik× | |
|---|---|---|
| Bidang | Perlombongan Teks | Perlombongan Teks |
| Keluarga | Process / pipeline | Process / pipeline |
| Tahun asal≠ | — | 2019 |
| Pengasas≠ | — | Nils Reimers & Iryna Gurevych (Sentence-BERT) |
| Jenis≠ | NLP text-generation / text-reduction task | NLP text-comparison task |
| Sumber perintis≠ | Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval. DOI ↗ | Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP. link ↗ |
| Alias≠ | automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme | semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi |
| Berkaitan | 4 | 4 |
| Ringkasan≠ | 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. | Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs. |
| ScholarGateSet data ↗ |
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