Structure-Aware Chunking for Abstractive Summarization of Long Legal Documents

AbstractThe efficacy of state-of-the-art abstractive summarization models is severely constrained by the extreme document lengths of legal judgments, which consistently surpass their fixed input capacities. The prevailing method, naive sequential chunking, is a discourse-agnostic process that induces context fragmentation and degrades summary coherence. This paper introduces Structure-Aware Chunking (SAC), a rhetorically-informed pre-processing pipeline that leverages the intrinsic logical structure of legal documents. We partition judgments into their constituent rhetorical strata—Facts, Arguments & Analysis, and Conclusion—prior to the summarization pass. We present and evaluate two SAC instantiations: a computationally efficient heuristic-based segmenter and a semantically robust LLM-driven approach. Empirical validation on the JUST-NLP 2025 L-SUMM shared task dataset reveals a nuanced trade-off: while our methods improve local, n-gram based metrics (ROUGE-2), they struggle to maintain global coherence (ROUGE-L). We identify this “coherence gap” as a critical challenge in chunk-based summarization and show that advanced LLM-based segmentation begins to bridge it.