The VTechAGP Dataset: A Benchmark for Academic-to-General-Audience Paraphrasing

I recently collaborated with Ming Cheng and Jiaying Gong, two members of the Machine Learning Laboratory research team led by Dr. Hoda Eldardiry. We created the VTechAGP dataset to support research on text simplification and paraphrase generation.

Motivation

Non-specialists often struggle with academic writing due to its discipline-specific jargon and rigid conventions. Text simplification research seeks to mitigate this challenge by reducing linguistic complexity, primarily through lexical and syntactic modifications. Existing datasets are restricted to sentence-level transformations and lack coverage across multiple domains, limiting their utility for broader applications. While domain-specific datasets, such as those in medicine and law, provide targeted resources, they do not support general-purpose simplification across disciplines. For a comparison of existing text simplification and paraphrase datasets, see Table 5 in the appendix of Cheng et al. (2024).¹

Institutional Context and Rationale

To address the lack of broadly applicable, document-level datasets, we propose VTechAGP,² which provides a parallel corpus that pairs full academic abstracts with their general-audience counterparts. These pairs were collected from VTechWorks, Virginia Tech’s institutional repository, which includes Electronic Theses and Dissertations (ETDs) along with other scholarly materials. The Graduate School’s ETD policies require students to submit both a traditional academic abstract and a general-audience abstract as part of their thesis or dissertation. Because these abstracts are written for distinct audiences but correspond to the same work, they provide a basis for analyzing differences in lexical complexity, syntactic variation, and semantic focus between academic and general-audience writing. By capturing these distinctions at the document level, the dataset serves as a resource for research in text simplification and domain adaptation in NLP.

Dataset Construction

To build the dataset, I collected academic and general audience abstracts from VTechWorks, using the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH). Each record includes:

• A traditional academic abstract.
• A corresponding general audience abstract.
• Metadata such as title, discipline, department, and degree information.

The dataset consists of paired academic and general-audience abstracts, allowing for document-level analysis of structural and linguistic differences. This enables potential applications in NLP for document-level paraphrasing and retrieval-based reformulation tasks.