Compara mètodes

	Resum de múltiples documents	Classificació de text	TF-IDF
Camp	Mineria de text	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	—	—	1988
Autor original	—	—	Salton & Buckley
Tipus	NLP text-summarization task	Supervised NLP classification task	Text vectorization / term-weighting scheme
Font seminal	Erkan, G. & Radev, D.R. (2004). LexRank: Graph-Based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Àlies	MDS, Çok Belgeli Özetleme (Multi-Document Summarization), multi-source summarization	text categorization, document classification, topic classification, metin sınıflandırma	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relacionats	5	4	3
Resum	Multi-document summarization (MDS) is a natural-language-processing task that condenses a cluster of related documents into a single comprehensive, coherent, and non-redundant summary. Formally described by Erkan and Radev (2004) through the LexRank algorithm, MDS is used in news cluster analysis, systematic literature reviews, and research synthesis to give readers a unified view of information spread across multiple sources.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.