by Cenveo Publisher Services
In recent years, publishers have begun to utilize advances in artificial intelligence (AI) and machine learning to significantly change their businesses for the better. Though some innovations, such as using an algorithm to predict if a book will be a bestseller, have gotten more news coverage, AI and machine learning has been incredibly useful to publishers to help process user data to better understand the marketplace, to improve peer review, to help with workflow, and even to create new products. Here are some of the most notable current trends we have observed:
One of the first implementations of AI in publishing, which is still being explored and is incredibly vital, is the use of AI to process big data, particularly surrounding usage of content. As publishers better understand how content is being used and why, they can better know what content to publish and in what format to better serve users.
Because peer review can sometimes create a log-jam for researchers while slowing down the publishing process, this seems like a good place for AI to be implemented to help boost efficiency. Last fall, Nature published an article about two services – StatReviewer and the partnership between ScholarOne and UNISLO – which are piloting programs that will analyze manuscripts, identify main points and arguments, and compare against an existing database of published articles to prevent against plagiarism, to highlight issues for a human reader, and more. While there seemed to be some initial skepticism from publishers and authors about this effectiveness, some are beginning to try out these systems.
Once a study has been published, other issues arise. Open Access has opened the floodgates for information, which is wonderful, but it has also sparked the need to better validate research. With the proliferation of studies being published in traditional channels, through self-publishing platforms, or even directly via social media, validation of research has become increasingly more difficult. On May 1st, a new platform called scite was launched, which uses “deep learning and a network of experts to analyze hundreds of millions of citation statements, classifying them as supporting, contradicting, or just mentioning, and presenting the results in an easy to understand interface.” This week, scite, Inc. won a National Science Foundation (NSF) Small Business Innovation Research grant to expand their work.
For publishers, the editing process can be incredibly time-consuming from editing for the larger ideas to the minutiae of line and copy editing. With AI and natural language processing (NLP), which features an algorithm trained to recognize the noun, verb, and object in a sentence, and to understand the structure of words in order to discern their meaning, publishers can automate simple editing and formatting tasks and focus their energy on adding greater value to the content. By using NLP, publishers can speed up the time to publication by 30-40%.
While there has been much written about newspapers using AI to write basic news stories and produce sports reporting, in academic publishing there are a lot of implications for how AI can help cull together research on a particular subject.
In April, Springer Nature announced that they had published their first machine-generated book in chemistry. The book is an auto-summarization of a large number of current research articles and is a product of a state-of-the-art algorithm Beta Writer which was developed to select and process relevant publications in this field from Springer Nature’s content platform SpringerLink.
“Looking back to a long tradition and expertise in academic book publishing, Springer Nature is aiming at shaping the future of book publishing and reading,” said Niels Peter Thomas, Managing Director Books at Springer Nature. “New technologies around Natural Language Processing and Artificial Intelligence offer promising opportunities for us to explore the generation of scientific content with the help of algorithms.”
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