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2. Automatic summarization - Wikipedia

   en.wikipedia.org/wiki/Automatic_summarization

   Abstractive summarization methods generate new text that did not exist in the original text. [12] This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express.

3. Notion (productivity software) - Wikipedia

   en.wikipedia.org/wiki/Notion_(productivity_software)

   Notion AI uses artificial intelligence, which enables prediction of some results, text generation and solving arithmetic. It will also work with other datasets in response to an input by the user of the AI. This AI can automate some tasks and assist the user to make a paragraph or outline from a line of text.

4. NovelAI - Wikipedia

   en.wikipedia.org/wiki/NovelAI

   NovelAI is an online cloud-based, SaaS model, and a paid subscription service for AI-assisted storywriting [2] [3] [4] and text-to-image synthesis, [5] originally launched in beta on June 15, 2021, [6] with the image generation feature being implemented later on October 3, 2022.

5. DeepSeek - Wikipedia

   en.wikipedia.org/wiki/DeepSeek

   DeepSeek (Chinese: 深度求索; pinyin: Shēndù Qiúsuǒ) is a Chinese artificial intelligence (AI) firm and family of Large Language Models based in Hangzhou. It is founded and backed by the Chinese hedge fund, High-Flyer. It has released its models as open source.

6. Open Neural Network Exchange - Wikipedia

   en.wikipedia.org/wiki/Open_Neural_Network_Exchange

   The Open Neural Network Exchange (ONNX) [ˈɒnɪks] [2] is an open-source artificial intelligence ecosystem [3] of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector.

7. Multi-document summarization - Wikipedia

   en.wikipedia.org/wiki/Multi-document_summarization

   Multi-document summarization is an automatic procedure aimed at extraction of information from multiple texts written about the same topic. The resulting summary report allows individual users, such as professional information consumers, to quickly familiarize themselves with information contained in a large cluster of documents.