AI Case Study

Clarivate Analytics aims to improve article peer review efficiency by adding natural language processing to its ScholarOne platform

Clarivate Analytics' has teamed up with UNSILO to add natural language processing abilities to its journal peer review software, ScholarOne. This allows papers submitted for review to be automatically summarised and checked against other papers for plagiarism.

Industry

Consumer Goods And Services

Media And Publishing

Project Overview

"ScholarOne, a peer-review platform used by many journals, is teaming up with UNSILO of Aarhus, Denmark, which uses natural language processing and machine learning to analyse manuscripts. UNSILO automatically pulls out key concepts to summarize what the paper is about. UNSILO uses semantic analysis of the manuscript text to extract what it identifies as the main statements... UNSILO then identifies which of these key phrases are most likely to be claims or findings, giving editors an at-a-glance summary of a study’s results. It also highlights whether the claims are similar to those from previously published papers, which could be used to detect plagiarism or simply to place the manuscript in context with related work in the wider literature."

Reported Results

In prototype phase currently; results not yet available.

Technology

Details not disclosed, but some form of natural language processing.

Function

Information Technology

Knowledge Management

Background

"Most researchers have good reason to grumble about peer review: it is time-consuming and error-prone, and the workload is unevenly spread, with just 20% of scientists taking on most reviews."

Benefits

Data

"UNSILO’s prototype gets information from the PubMed Central scholarly database, which lets it compare new manuscripts with the full text of 1.7 million published biomedical research papers — a large, but limited, data set. The company says it will soon add more than 20 million further PubMed papers."