Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning

• URL: http://arxiv.org/abs/2509.17921v2
• Date: Wed, 15 Oct 2025 16:50:15 GMT
• Title: Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning
• Authors: Zhenyun Deng, Yulong Chen, Andreas Vlachos,
• Abstract summary: We propose a framework for zero-shot decontextualisation, which determines what content should be mentioned and in what order for a sentence to be understood out of context.<n>We identify potentially ambiguous units from the given sentence, and extract relevant units from the context based on their discourse relations.<n>Finally, we generate a content plan to rewrite the sentence by enriching each ambiguous unit with its relevant units.
• Score: 15.992477600061166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting individual sentences from a document as evidence or reasoning steps is commonly done in many NLP tasks. However, extracted sentences often lack context necessary to make them understood, e.g., coreference and background information. To this end, we propose a content selection and planning framework for zero-shot decontextualisation, which determines what content should be mentioned and in what order for a sentence to be understood out of context. Specifically, given a potentially ambiguous sentence and its context, we first segment it into basic semantically-independent units. We then identify potentially ambiguous units from the given sentence, and extract relevant units from the context based on their discourse relations. Finally, we generate a content plan to rewrite the sentence by enriching each ambiguous unit with its relevant units. Experimental results demonstrate that our approach is competitive for sentence decontextualisation, producing sentences that exhibit better semantic integrity and discourse coherence, outperforming existing methods.

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