Overview of the CLEF 2023 SimpleText Lab: Automatic Simplification of Scientific Texts

Liana Ermakova, Eric SanJuan, Stéphane Huet, Hosein Azarbonyad, Olivier Augereau, Jaap Kamps

Abstract: There is universal consensus on the importance of objective scientific information, yet the general public tends to avoid scientific literature due to access restrictions, its complex language or their lack of prior background knowledge. Academic text simplification promises to remove some of these barriers, by improving the accessibility of scientific text and promoting science literacy. This paper presents an overview of the CLEF 2023 SimpleText track addressing the challenges of text simplification approaches in the context of promoting scientific information access, by providing appropriate data and benchmarks, and creating a community of IR and NLP researchers working together to resolve one of the greatest challenges of today. The track provides a corpus of scientific literature abstracts and popular science requests. It features three tasks. First, content selection (what is in, or out?) challenges systems to select passages to include in a simplified summary in response to a query. Second, complexity spotting (what is unclear?) given a passage and a query, aims to rank terms/concepts that are required to be explained for understanding this passage (definitions, context, applications). Third, text simplification (rewrite this!) given a query, asks to simplify passages from scientific abstracts while preserving the main content.