A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

2026-07-28

AI Summary

This study addresses the challenge of scientific papers being inaccessible to non-specialist readers due to linguistic complexity. The authors propose a human–AI collaborative simplification pipeline that first leverages GPT-4o-mini to generate initial simplified summaries, followed by iterative refinement through a two-stage feedback loop involving both lay readers and domain experts. This approach innovatively integrates dual human feedback mechanisms to simultaneously enhance readability and preserve terminological accuracy. The project also introduces the first corpus of scientifically simplified texts designed for interdisciplinary communication, annotated with both human judgments and automatic evaluation metrics. Experimental results demonstrate that LLM-generated simplifications are consistently preferred for their clarity and conciseness, while expert editing effectively retains essential technical terms and the strength of scientific claims.

Abstract

Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language model (LLM)-based simplification of scientific texts and present a human-in-the-loop workflow that transforms expert summaries into more accessible versions for non-specialists. Using SciSummNet as the source corpus, we first generate baseline simplifications with GPT-4o-mini. In Phase 1, readers from STEM fields outside computer science identify difficult sentences and phrases and compare the original and GPT-simplified summaries in terms of comprehensibility, naturalness, and simplicity. In Phase 2, computer science experts use this feedback to create expert-edited reference simplifications. We release the resulting corpus together with human judgments and automatic evaluation results. The Phase 1 judgments show a clear preference for the GPT-generated summaries in terms of comprehensibility and simplicity, while qualitative analysis of the Phase 2 edits highlights the importance of preserving domain-specific terminology and the strength of scientific claims. The resulting resource supports the training and benchmarking of simplification systems for cross-disciplinary scientific communication.

Problem

• scientific text simplification
• cross-disciplinary communication
• accessibility of scientific literature
• non-specialist comprehension

Innovation

• human-in-the-loop
• scientific text simplification
• large language models
• cross-disciplinary communication
• expert-edited corpus