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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.
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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.
The free model uses the company's standalone LLM based on GPT-3.5 with browsing. [7] [8]It uses the context of the user queries to provide a personalized search result. Perplexity summarizes the search results and produces a text with inline citat
Smallpdf is a Swiss online web-based PDF software, founded in 2013. [2] It offers free version with limited features to compress, convert and edit PDF documents. [3] And its paid version offers advanced features like OCR, compress, and more [4].
Summarizing a reliable source. This is inherently risky, due to the likelihood of an LLM introducing original research or bias that was not present in the source, as well as the risk that the summary may be an excessively close paraphrase, which would constitute plagiarism. You must proactively ensure such a summary complies with all policies.
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