From Information to Insight: Leveraging LLMs for Open Aspect-Based Educational Summarization

Anonymous

Abstract: This paper addresses the challenge of aspect-based summarization in education by introducing Reflective ASPect-based summarization (ReflectASP), a dataset that summarizes student reflections on STEM lectures. Despite the promising performance of large language models in general summarization, their application to nuanced, aspect-specific summaries in educational texts remains under-explored. ReflectASP eases the exploration of open-aspect-based summarization (OABS), overcoming the limitations of current datasets and annotation complexities. We leverage GPT-4 for generating reference summaries and propose a self-refine framework to enhance summary quality. Our work benchmarks the capabilities of different language models in this novel context, contributing a unique dataset and insights into effective summarization strategies for educational content. We will make our model, dataset, and all human evaluation results available at {url annonymized_for_review}.

Paper Type: long

Research Area: NLP Applications

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Data resources, Data analysis

Languages Studied: English