Simplifying Scholarly Abstracts for Accessible Digital Libraries

TL;DR

This paper presents a method for fine-tuning language models to simplify scholarly abstracts, making scientific literature more accessible to a broader audience, including those with lower reading levels.

Contribution

The authors created a specialized corpus and fine-tuned multiple models to improve abstract readability while preserving content fidelity, offering a more accessible alternative to commercial models.

Findings

Models improved readability by over three grade levels.

Models maintained semantic fidelity to original abstracts.

Proposed models are more compact and privacy-preserving.

Abstract

Standing at the forefront of knowledge dissemination, digital libraries curate vast collections of scientific literature. However, these scholarly writings are often laden with jargon and tailored for domain experts rather than the general public. As librarians, we strive to offer services to a diverse audience, including those with lower reading levels. To extend our services beyond mere access, we propose fine-tuning a language model to rewrite scholarly abstracts into more comprehensible versions, thereby making scholarly literature more accessible when requested. We began by introducing a corpus specifically designed for training models to simplify scholarly abstracts. This corpus consists of over three thousand pairs of abstracts and significance statements from diverse disciplines. We then fine-tuned four language models using this corpus. The outputs from the models were subsequently examined both quantitatively for accessibility and semantic coherence, and qualitatively for language quality, faithfulness, and completeness. Our findings show that the resulting models can improve readability by over three grade levels, while maintaining fidelity to the original content. Although commercial state-of-the-art models still hold an edge, our models are much more compact, can be deployed locally in an affordable manner, and alleviate the privacy concerns associated with using commercial models. We envision this work as a step toward more inclusive and accessible libraries, improving our services for young readers and those without a college degree.