Automatic Generation of Highlights for Academic Paper Via Prompt-based Learning

A new study proposes using prompt-based learning to automatically generate highlights for academic papers, a task that has traditionally required large labeled datasets and supervised training methods [1]. The research, submitted to arXiv on June 24, 2026, evaluates several language models including locally deployed GPT-2 and T5, as well as ChatGPT accessed through an API [1]. Highlights are concise summaries of a paper's main contributions, but many journals do not require them, limiting their utility in literature retrieval and bibliometric analysis [1]. arXiv itself is an open-access repository that hosts preprints across mathematics, physics, computer science and other fields, and as of November 2024 receives about 24,000 submissions per month [7]. Prompt engineering is the practice of designing and refining input instructions to produce more accurate outputs from generative AI models [3]. The study's authors designed task-specific prompt templates and combined them with paper abstracts as model inputs [1]. Experiments on three datasets showed that ChatGPT with prompt templates achieved performance comparable to previous supervised methods without using task-specific training samples [1]. When a small number of examples were added to the prompts, the model significantly outperformed state-of-the-art methods on two datasets [1]. The researchers found that ChatGPT's performance on this task is highly sensitive to the information provided in the prompt, despite its strong language modeling ability [1]. Case studies indicated the generated highlights were generally coherent, informative, and close to author-written highlights [1]. Large language models like those used in the study are neural networks trained on vast amounts of text for natural language generation and other tasks, and are the foundational technology behind modern chatbots [4]. The proposed method does not rely on domain-specific training corpora and can generate highlights for papers that lack such information [1]. The study is among the first to apply prompt-based learning to academic highlight generation [1]. While the technique shows promise, broader discussions around AI deployment have raised concerns about algorithmic bias, where systematic errors can privilege one category over another due to factors such as imbalanced training data or design choices [6].