Comparar métodos

	Resumen de texto	Análisis de Sentimiento
Campo	Minería de texto	Minería de texto
Familia	Process / pipeline	Process / pipeline
Año de origen	—	—
Autor original	—	—
Tipo	NLP text-generation / text-reduction task	NLP text-classification task
Fuente seminal	Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Alias	automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme	opinion mining, polarity detection, duygu analizi
Relacionados	4	3
Resumen	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.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.