Efficient Scientific Paper Summarization Using Unsupervised Extraction and Transformer-Based Abstraction

Abstract

The growing volume of scientific literature has driven the demand for automated text summarization systems that are natural and factual. Extractive Text Summarization methods are factually accurate in meaning; still, they can lead to a summary that is not cohesive. On the other hand, abstractive summarization systems improve readability but may introduce factual bias. The paper overcomes these shortcomings by creating a hybrid text summarization system that combines extractive and abstractive methods to maximize both quality and factual content. The framework uses two unsupervised extractive models, HipoRank and PacSum, to extract important sentences, which are then synthesized with the original input document's introduction section and subjected to long-document transformer models, PEGASUSX and LED, to generate abstract-style summaries. Among the tested combinations, the HipoRank-LED configuration achieved the most balanced performance, with ROUGE-1: 0.440, ROUGE-2: 0.220, and ROUGE-L: 0.410 on the PubMed dataset. This combination occasionally produced summaries with greater abstractiveness than the human-written references. Various experiments across the ScisummNet, ArXiv, and PubMed datasets show that hybrid configurations are always better than extractive and abstractive ones. HipoRank-LED is the most efficient model, with ROUGE-1 = 0.440, ROUGE-2 = 0.220, and ROUGE-L = 0.410 on PubMed. Results indicate that combining extractive grounding with long-context transformers improves informativeness and coherence and reduces hallucination errors. The introduction-guided structured input also provides better global context for summarizing complex scientific documents. The findings indicate that the transformer-based abstraction, combined with an extractive text summarization approach, can be a very useful, scalable, and domain-independent model for approximating long scientific texts.