VT ech AGP : An Academic-to-General-Audience Text Paraphrase Dataset and Benchmark Models (2025)

Abstract: Existing text simplification or paraphrase datasets mainly focus on sentence-level text generation in a general domain. These datasets are typically developed without using domain knowledge. In this paper, we release a novel dataset, VTechAGP, which is the first academic-to-general-audience text paraphrase dataset consisting of document-level these and dissertation academic and general-audience abstract pairs from 8 colleges authored over 25 years. We also propose a novel dynamic soft prompt generative language model, DSPT5. For training, we leverage a contrastive-generative loss function to learn the keyword vectors in the dynamic prompt. For inference, we adopt a crowd-sampling decoding strategy at both semantic and structural levels to further select the best output candidate. We evaluate DSPT5 and various state-of-the-art large language models (LLMs) from multiple perspectives. Results demonstrate that the SOTA LLMs do not provide satisfactory outcomes, while the lightweight DSPT5 can achieve competitive results. To the best of our knowledge, we are the first to build a benchmark dataset and solutions for academic-to-general-audience text paraphrase dataset. Models will be public after acceptance.

## introduction text generation aims to produce understandable text in human language from various sources of input data. among them, text-to-text generation remains an important and challenging task with extensive applications such as language translation @xcite , paraphrase generation @xcite ), text simplification @xcite , etc. existing text simplification and paraphrase generation datasets (shown in in this paper, we pioneer the research of academic-to-general-audience text generation by introducing a new benchmark dataset vtechagp, which is derived from electronic theses and dissertations (etds) at virginia tech over twentyfive years. vtechagp consists of documentlevel abstract pairs (academic abstract and generalaudience abstract). vtechagp also provides other information such as title, discipline, degree level, etc. this auxiliary information shows the potential of vtechagp for other tasks such as topic generation, etc. in addition, the abstracts in vtechagp come from multiple domains (colleges) and vtechagp is labeled with each specific domain and provides the domain knowledge keywords. more details about vtechagp are presented in sec. 3 and based on vtechagp, we evaluate several sota pre-trained large language models (llms), such as llama2 @xcite , claude2 @xcite , chatgpt @xcite , etc., to establish the baseline performance. however, these sota pre-trained llms have demonstrated the following limitations: (1) some llms do not provide public apis, or the apis are not free. also, some llms (e.g., claude2) do not provide fine-tuning, making them less adaptable to specific tasks. (2) the model size of llms is very large. for example, llama2 has about 65 billion parameters. fine-tuning these llms is resource-intensive in terms of memory and computation time. even the inference implementation requires more memory and time. (3) the pre-trained llms do not show competitive performance for the academic-to-general-audience text paraphrasing task on vtechagp in sec. 5. to address the above challenges, we propose dspt5, a dynamic soft prompt-based generative model with the crowd sampling decoding strategy during the inference stage. dspt5 is built based on the pre-trained t5 @xcite , which has only about 220 million parameters. in particular, the dynamic soft prompt template in dspt5 can automatically adapt to different academic domains by changing the keywords extracted from the academic abstract. the prompt encoder in dspt5 is trained to generate and fine-tune keyword vectors combined with the dynamic prompt template. to this end, we design a hybrid loss function with generative language model loss and contrastive loss to jointly learn the generated text representations as well as the ability to distinguish technical keywords from non-technical keywords. during inference, dspt5 employs two alignment functions at both the semantic and structural levels to select the best candidate for the final generated output. the contributions can be summarized as follows: (1) dataset: we construct vtechagp, the first academic-to-general-audience text paraphrase dataset. vtechagp is a document-level text generation dataset with multiple technical domains. (2) baselines: we implement several sota llms as benchmarks to compare the performance with our proposed model dspt5. experimental results show that there is still a huge room for further improvement of the existing llms. (3) approach: we propose a lightweight model, dspt5, which utilizes dynamic soft prompts with a hybrid loss function and a new crowd decoding strategy. experimental results show that dspt5 can achieve competitive results with sota llms. (4) evaluation: we explore various evaluation metrics for the academic-to-general-audience text paraphrasing task on vtechagp from different perspectives, including document-level embedding-based, wordbased, and end-to-end metrics. in addition, simplicity, diversity, readability, and toxicity are also considered for the performance evaluation. ## approach figure 1 shows our proposed dspt5 with two main components: dynamic soft prompt generation with hybrid loss and crowd sampling decoding strategy. ## conclusion we created vtechagp foot_2 , an academic-to-generalaudience document-level translation benchmark dataset, which consists of academic and general audience abstract pairs with their corresponding auxiliary information such as title, subject terms, etc. we explore several sota llms to establish the baseline performance on the text paraphrasing task. we propose a new model, dspt5, which includes dynamic soft prompt generation with hybrid loss during the training phase and a new crowdsampling strategy in the inference stage. we evaluate the datasets and models from the perspective of document-level embedding-based, wordbased, end-to-end metrics, simplicity, diversity, readability, toxicity, and human evaluation. extensive experimental results on 8 different colleges of vtechagp show that dspt5 achieves comparable results with other sota benchmark models.