So sánh phương pháp

	Trích xuất từ khóa	Mô hình hóa chủ đề bằng NMF	TF-IDF
Lĩnh vực	Khai phá văn bản	Khai phá văn bản	Khai phá văn bản
Họ	Process / pipeline	Process / pipeline	Process / pipeline
Năm ra đời	—	1999	1988
Người khởi xướng	—	Lee & Seung	Salton & Buckley
Loại	NLP text-mining task	Matrix-factorization topic model	Text vectorization / term-weighting scheme
Công trình gốc	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Lee, D.D. & Seung, H.S. (1999). Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788-791.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Tên gọi khác	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	non-negative matrix factorization topic modeling, NMF topics, Konu Modelleme — NMF	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Liên quan	4	4	3
Tóm tắt	Keyword extraction is a natural-language-processing task that automatically identifies the words or phrases that best represent the content of a document. It turns a body of free text into a compact, ranked list of key terms, drawing on statistical, graph-based methods such as TextRank (Mihalcea & Tarau, 2004), or embedding-based methods such as KeyBERT (Grootendorst, 2020).	NMF topic modeling uses Non-negative Matrix Factorization — the parts-based decomposition introduced by Lee and Seung (1999) — to extract document-topic distributions from a corpus. By factoring a document-term matrix into two non-negative matrices, it recovers a small set of topics and tends to produce more interpretable topics than LDA.	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.