A 247-page publication about lithium-ion batteries is the first textbook authored by a computer, setting a new milestone in machine-generated publishing. Springer, a leading publisher, collaborated with researchers at the Applied Computational Linguistics (ACoLi) lab at Goethe University in Frankfurt to produce the book.
Springer says that beyond the preface, everything in the book was algorithmically generated. They suggest their method signals ‘a new era in scientific publishing.’ Springer intends to apply lessons learned to automate publishing more books in other fields, including the humanities and social sciences.
The rationale: Research and development in rechargeable power supplies is moving quickly. Researchers want to either keep up or get up-to-speed quickly, and the volume of published findings is daunting. More than 53,000 research papers in the field have been published in the past three years.
The achievement: Autoscribe systems in news typically raw data from structured databases and then fit it into template-driven stories. This system assessed 1,200 possible papers in Springer’s files and shortlisted them to 150. It went on and organized them by theme, then extracted, summarised, aggregated, or paraphrased the content as required. The system also handled structural elements, such as bibliographies, scientific notation, introductions, and table of contents for the 247 word book.
- This changes the scale for automated text that’s ready-made for the marketplace. News autoscribe systems are averaging 100-400 words or graphics, with templated output. The Springer system produced more than 200 pages drawn from complete papers.
- The workflow creates a platform for more titles. Other kinds of longer-form NLP publications are inevitable.
- It’s another industry + academic success story. Industry identifies a market-driven need and data to work to work with, the research community provides know-how and specialty computing for a high-profile outcome. Both gain practical learning, profile, and possibilities for revenue or future spin-offs.
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