1. The limited context window of LLM poses challenges when summarizing large documents. A common strategy (provided by LangChain or LlamaIndex) is to: (i) divid

1. The limited context window of LLM poses challenges when summarizing large documents. A common strategy (provided by LangChain or LlamaIndex) is to: (i) divide the document into chunks, (ii) summarize each chunk using LLM, and (iii) combine them into one global summary using LLM. 2. For document division, a bad chunk size can result in the last chunk being much smaller than the previous ones. As a result, the global summary might overemphasize the last chunk and thus bias away from the core semantics of the document. See https://tinyurl.com/vectifyai-summarization https://tinyurl.com/vectifyai-summarization for an example. 3. In our blog, we introduce a straightforward method to automatically decide the optimal chunk size for different documents. We find this new strategy significantly reduces bias in the global summary. The implementation can be found at https://github.com/VectifyAI/LargeDocumentSummarization https://github.com/VectifyAI/LargeDocumentSummarization. Stay tuned at Vectify AI (https://vectify.ai https://vectify.ai) for more innovative techniques to turbocharge your LLM pipelines!