Editors often use LLMs to summarize long interviews or reports, but models frequently omit crucial context, such as conditions, caveats, and causal links. A case study involving a recommendation module in 12 stores over eight weeks showed that while a model might retain figures like a 7% increase in average check, it often misattributes the cause by ignoring concurrent promotions.
To ensure accuracy, editors should perform a two-step verification process. First, check for completeness by ensuring all essential facts from the source are present in the summary. Second, verify confirmability by checking that every claim in the summary is directly supported by the source text. This helps identify both missing information and hallucinations.
Large context windows, such as one million tokens, do not guarantee that a model will process every page correctly. Models often prioritize information at the beginning and end of a document, potentially losing critical details located in the middle. Furthermore, file formats like PDF or DOCX can cause data loss if the system fails to parse tables, images, or footnotes correctly.
Six models—ChatGPT, Claude, Gemini, DeepSeek, YandexGPT, and GigaChat—offer different capabilities for text processing. For instance, Claude is noted for its ability to handle large PDF documents with visual elements, while Gemini and others provide API features like JSON output or citation support to help maintain factual integrity during the summarization process.
Editors should test models by selecting pages containing tables or charts to verify if the system correctly extracts data and footnotes. If the model fails to retrieve these, the issue lies in the ingestion process rather than the summarization logic. Establishing a consistent pipeline with specific model versions and structured output formats is recommended for professional workflows.
