Summarizing text is one of the main use cases for large language models. Clients often want to summarize articles, financial documents, chat history, tables, pages, books, and more. We all expect that LLM will distill only the important pieces of information, especially from long texts. However, this isn't always possible with the expected level of quality. Even a larger token limit isn’t a guaranteed solution. Fortunately, there are approaches that help summarize texts of different lengths - whether it’s a couple of sentences, paragraphs, pages, an entire book, or an unknown amount of text.
Dmitry Baraishuk
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6
min read