Jämför metoder

	Multi-Document Summarization	BERT-inbäddningar	Sentimentanalys	TF-IDF
Ämnesområde	Textutvinning	Textutvinning	Textutvinning	Textutvinning
Familj	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Ursprungsår	—	2019	—	1988
Upphovsperson	—	Devlin, Chang, Lee & Toutanova (Google AI)	—	Salton & Buckley
Typ	NLP text-summarization task	Contextual transformer text-representation method	NLP text-classification task	Text vectorization / term-weighting scheme
Ursprungskälla	Erkan, G. & Radev, D.R. (2004). LexRank: Graph-Based Lexical Centrality as Salience in Text Summarization. Journal of Artificial Intelligence Research, 22, 457-479.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Alias	MDS, Çok Belgeli Özetleme (Multi-Document Summarization), multi-source summarization	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Närliggande	5	4	3	3
Sammanfattning	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.	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.