Σύγκριση μεθόδων

	Περίληψη Κειμένου	Εξαγωγή λέξεων-κλειδιών	Σημασιολογική Ομοιότητα
Πεδίο	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου	Εξόρυξη Κειμένου
Οικογένεια	Process / pipeline	Process / pipeline	Process / pipeline
Έτος προέλευσης	—	—	2019
Δημιουργός	—	—	Nils Reimers & Iryna Gurevych (Sentence-BERT)
Τύπος	NLP text-generation / text-reduction task	NLP text-mining task	NLP text-comparison task
Θεμελιώδης πηγή	Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.	Mihalcea, R. & Tarau, P. (2004). TextRank: Bringing Order into Texts. EMNLP, 404-411.	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.
Εναλλακτικές ονομασίες	automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme	keyphrase extraction, key term extraction, Anahtar Kelime Çıkarma (Keyword Extraction)	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi
Συναφείς	4	4	4
Σύνοψη	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.	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).	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.