Compara mètodes

	Resum de text	Similitud Semàntica
Camp	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline
Any d'origen	—	2019
Autor original	—	Nils Reimers & Iryna Gurevych (Sentence-BERT)
Tipus	NLP text-generation / text-reduction task	NLP text-comparison task
Font seminal	Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.
Àlies	automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi
Relacionats	4	4
Resum	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.