手法を比較

	テキスト要約	感情分析
分野	テキストマイニング	テキストマイニング
系統	Process / pipeline	Process / pipeline
提唱年	—	—
提唱者	—	—
種類	NLP text-generation / text-reduction task	NLP text-classification task
原典	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.
別名	automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme	opinion mining, polarity detection, duygu analizi
関連	4	3
概要	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.