เปรียบเทียบวิธี

	Retrieval-Augmented Generation (RAG)	การสรุปความอัตโนมัติ
สาขาวิชา	การทำเหมืองข้อความ	การทำเหมืองข้อความ
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	2020	—
ผู้ริเริ่ม	Lewis, Patrick et al. (Meta AI / Facebook AI Research)	—
ประเภท	Hybrid retrieval + generation pipeline	NLP text-generation / text-reduction task
แหล่งต้นตำรับ	Lewis, P. et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. Advances in Neural Information Processing Systems (NeurIPS), 33, 9459-9474.	Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.
ชื่อเรียกอื่น	RAG, retrieval-augmented LLM, grounded generation, Erişim Destekli Metin Üretimi (RAG)	automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme
ที่เกี่ยวข้อง	7	4
สรุป	Retrieval-Augmented Generation (RAG) is a natural-language-processing pipeline introduced by Lewis et al. in 2020 that strengthens a large language model (LLM) with evidence fetched at inference time from an external knowledge base. Instead of relying solely on what a model memorised during training, RAG first retrieves the most relevant passages from a document index and then hands those passages to the LLM as context, grounding the generated answer in verifiable, up-to-date information. The approach reduces hallucination and allows domain-specific or time-sensitive knowledge to be injected without retraining the model.	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.