@inproceedings{arulanandam2026section,
  title={{Section-Weighted Hybrid Approach for Legal Case Retrieval}},
  author={Arulanandam, Rajith and De Silva, Nisansa},
  booktitle={2026 8th International Conference on Natural Language Processing (ICNLP)},
  pages={567--571},
  year={2026},
  abstract={Finding truly analogous precedents requires capturing legal reasoning beyond surface word overlap. We present a two-stage, section-aware framework for legal case retrieval that first segments raw judgments into facts, issues, decision, and reasoning using a deterministic large language model (LLM) offline. In Stage 1, we combine parallel lexical (BM25) and semantic (dense ANN) whole-document searches via Reciprocal Rank Fusion (RRF) to form a high-recall candidate pool. In Stage 2, we perform fine-grained, like-for-like comparisons (e.g., query reasoning vs. candidate reasoning). To address the scale mismatch between unbounded lexical scores and cosine similarities, we apply query-wise Z-score normalization before aggregating signals with learned section weights. For the top results, the system returns the relevant section text with a concise, grounded rationale and party-stance labels. We evaluate on a jurisdiction-scale benchmark, demonstrating consistent gains over strong lexical and neural baselines while maintaining high candidate coverage.},
  doi={10.1109/ICNLP69856.2026.11527762},
  organization={IEEE},
  misc={https://arxiv.org/pdf/2606.03138,NLP:ML:LAW,https://goo.gl/iY6aTr}
}