OpenAI has developed an AI model that can summarize books of arbitrary length. A fine-tuned version of the research lab’s GPT-3, the model works by first summarizing small sections of a book and then summarizing those summaries into higher-level summaries, following a paradigm OpenAI calls “recursive task decomposition.”
Summarizing book-length documents could be valuable in the enterprise, particularly for documentation-heavy industries like software development. A survey by SearchYourCloud found that workers take up to eight searches to find the right document, and McKinsey reports that employees spend 1.8 hours every day — 9.3 hours per week, on average — searching and gathering job-related information.
“OpenAI believes that this is an effective ‘recipe’ that can be used to help humans supervise many other tasks,” a spokesperson told VentureBeat via email. “A scalable solution to the alignment problem needs to work on tasks that are difficult or time-consuming for humans to evaluate.”
OpenAI is far from the first to apply AI to the problem of summarization. Startups like Primer use machine learning techniques to help parse and collate a large number of documents across several languages. Google has investigated summarization methods that can generate abstract summaries of paragraphs — as has Microsoft. And Facebook is reportedly developing an AI tool that summarizes news articles so users don’t have to read them.