GravRank: A Gravitational Extractive Preprocessing Framework for Abstractive Summarization of Long Documents

Transformer-based models face persistent challenges in long-document summarization due to fixed input-length constraints. Hybrid approaches address this limitation by applying extractive preprocessing to select salient sentences for downstream abstractive summarization. However, many unsupervised extractive methods, including TextRank and LexRank, rely on heuristic graph centrality and often struggle to preserve semantic coherence or control redundancy. This paper proposes GravRank, an unsupervised and deterministic extractive summarization framework that models sentence importance as an emergent property of pairwise semantic interactions governed by a softened Plummer potential. Sentences are embedded in a shared semantic space, and a global energy function is defined over all sentence pairs using a softened interaction kernel. This formulation jointly encodes relevance and redundancy within a single scoring function, avoiding iterative graph propagation, supervised training, and post hoc diversity filtering. The deterministic extractive output is used as input to a BART-based abstractive summarization model, forming a hybrid pipeline for long and semantically dense documents. Experiments on the BillSum, PubMed, and GovReport datasets show that GravRank improves over classical unsupervised baselines, remains competitive with recent extractive methods, and yields a competitive result in downstream abstractive summarization when combined with BART.