2024 EMNLP EMNLP 2024

Cochrane-auto: An Aligned Dataset for the Simplification of Biomedical Abstracts

Abstract

AbstractThe most reliable and up-to-date information on health questions is in the biomedical literature, but inaccessible due to the complex language full of jargon. Domain specific scientific text simplification holds the promise to make this literature accessible to a lay audience. Therefore, we create Cochrane-auto: a large corpus of pairs of aligned sentences, paragraphs, and abstracts from biomedical abstracts and lay summaries. Experiments demonstrate that a plan-guided simplification system trained on Cochrane-auto is able to outperform a strong baseline trained on unaligned abstracts and lay summaries. More generally, our freely available corpus complementing Newsela-auto and Wiki-auto facilitates text simplification research beyond the sentence-level and direct lexical and grammatical revisions.

🌉 Interdisciplinary Bridge — Computer Science and Healthcare & Medicine and Natural Language Processing
🧭 Keyword Pioneer — aligned dataset
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Speech & Audio