Is this research paper too long to read? AI can summarize it for you

This free tool, known as TLDR (the Internet acronym for "Too long, didn't read"), condenses a study into a concise, single-sentence summary. It does so by focusing on the paper's salient information from the abstract, introduction, and conclusion sections. This software removes things like methodological details, which are usually summarized in the abstract.

Researchers and engineers from the non-profit Allen Institute for Artificial Intelligence (AI2) in Seattle, Washington, have used GPT-3 style neuro-linguistic programming techniques to create this AI-powered model. They trained it with a dataset of over 5,411 computer science papers with matching summaries, some written by the team itself and others by a class of undergraduate students from the University of Washington. They further improved the performance of the TLDR model by gathering training examples in 16 other fields.

The tool has been activated for search results at Semantic Scholar, a search engine created by AI2. For the moment, it is only available in beta for more than 45 million English-language papers across computer science, biology, and medicine. Researchers are currently improving the software so that it expands to more languages and domains in the coming months.

For Daniel S. Weld, who manages the Semantic Scholar group at AI2, one-sentence summaries of research papers could help scientists to make quick informed decisions about which papers are worth further reading. "People often ask why are TLDRs better than abstracts, but the two serve completely different purposes. Since TLDRs are 20 words instead of 200, they are much faster to skim," he said in a statement.

Keeping it short and sweet... and funny

AI2's TLDR software is not the only scientific summarizing tool that cuts through academic jargon. Academics recently shared on Twitter AI-powered summaries of their research papers that "a second grader can understand," courtesy of tl;dr papers. This website was created by software engineers Yash Dani and Cindy Wu, according to Professor Michelle Ryan, director of the Global Institute for Women's Leadership at the Australian National University.

Ryan posted on her Twitter account the AI summary of one of her articles on "glass cliff," a form of gender discrimination in which women are appointed in leadership roles when companies are at greatest risk of failure. But that's not how AI would explain it. According to tl;dr papers, "glass cliff is a place where a lot of women get put. It's a bad place to be."

Dr. Laura Sockol, a clinical psychologist and associate professor at Davidson College, also shared the summarized version of her article, "Improving Quantitative Abilities and Attitudes in Clinical Psychology Courses." For the machine, this research paper focuses on the fact that "when students take psychological classes, many don't like learning about statistics." Why so? Because "they are bad at it and they are afraid of it."