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Learning Long-Document Embeddings via Chunk–Context Entailment

Learning faithful embeddings for long documents remains challenging, especially in domains like law and medicine where inputs are long, structured, and semantically heterogeneous. We introduce the Chunk Prediction Encoder (CPE), a self-supervised framework that treats chunk–context compatibility as an unsupervised NLI problem. Given a document, CPE masks a chunk and learns (i) a contrastive objective that aligns the masked document with its held-out chunk against in-batch negatives, and (ii) a binary entailment head that predicts whether a candidate chunk belongs to the document. This joint objective encourages both geometric smoothness and directional semantic consistency, yielding robust document-level embeddings. We evaluate CPE with hierarchical and sparse-attention backbones on five benchmarks spanning legal and biomedical domains under frozen-embedding and end-to-end fine-tuning protocols. CPE consistently outperforms baselines, and is more compute-efficient than prompt-only LLM baselines under matched token budgets. Ablations demonstrate the effect of chunk length, the contrastive-vs-entailment balance, and skimming strategies.