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

2026-06-24 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, medium

Summary

This study investigates prompt-based learning for the automatic generation of highlights for academic papers, a task crucial for literature retrieval and bibliometric analysis. Researchers designed task-specific prompt templates, combining them with paper abstracts as inputs for various language models, including locally deployed GPT-2 and T5, and API-accessed ChatGPT. Experiments across three datasets demonstrated that ChatGPT, utilizing these prompt templates, achieved performance comparable to existing supervised methods without requiring task-specific training data. Furthermore, incorporating a small number of examples into the prompts led to ChatGPT significantly outperforming state-of-the-art methods on two of the datasets. The analysis also revealed that while ChatGPT possesses strong language modeling capabilities, its performance on highlight generation is highly sensitive to the specific information provided within the prompt design. This approach is notable for not relying on domain-specific training corpora, enabling highlight generation for papers lacking such information.

Key takeaway

For NLP Engineers or AI Scientists tasked with automating academic content summarization, you should consider prompt-based learning with large language models like ChatGPT. This approach allows you to generate coherent and informative paper highlights without the need for large, domain-specific training corpora. Focus on carefully designing your prompt templates and incorporating a small number of in-context examples, as this significantly boosts performance and can outperform traditional supervised methods.

Key insights

Prompt-based learning with LLMs can generate academic paper highlights effectively without extensive labeled data.

Principles

• Prompt design critically impacts LLM performance.
• Few-shot prompting enhances generation quality.
• Domain-specific training data is not always required.

Method

Design task-specific prompt templates, combine with paper abstracts, and input to large language models like ChatGPT for highlight generation.

In practice

• Use ChatGPT with tailored prompts for highlight generation.
• Include few-shot examples in prompts for better results.
• Experiment with prompt wording to optimize output.

Topics

• Prompt Engineering
• Large Language Models
• Academic Highlight Generation
• ChatGPT
• Text Summarization
• Few-shot Learning

Best for: Research Scientist, AI Scientist, NLP Engineer, Prompt Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.