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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. Semantic Scholar - Wikipedia

   en.wikipedia.org/wiki/Semantic_Scholar

   Semantic Scholar is a research tool for scientific literature powered by artificial intelligence. It is developed at the Allen Institute for AI and was publicly released in November 2015. [ 2 ] Semantic Scholar uses modern techniques in natural language processing to support the research process, for example by providing automatically generated ...

4. 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.

5. I tried over 15 AI tools this year. These are the ones I ...

   www.aol.com/tried-over-15-ai-tools-183143435.html

   This year, I tried more than 15 AI tools and features ranging from chatbots to virtual shopping assistants and smart mirrors. The variety is an indication of just how widespread AI is becoming and ...

6. AI Writes Scientific Papers That Sound Great—but Aren ... - AOL

   www.aol.com/lifestyle/ai-writes-scientific...

   Writing review articles, for example, is a task well suited to AI: it involves sifting through the existing research on a subject, analyzing the results, reaching a conclusion about the state of ...

7. LangChain - Wikipedia

   en.wikipedia.org/wiki/LangChain

   LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.