Enhancing Long Document Long Form Summarisation with Self-Planning

• URL: http://arxiv.org/abs/2512.17179v1
• Date: Fri, 19 Dec 2025 02:37:30 GMT
• Title: Enhancing Long Document Long Form Summarisation with Self-Planning
• Authors: Xiaotang Du, Rohit Saxena, Laura Perez-Beltrachini, Pasquale Minervini, Ivan Titov,
• Abstract summary: We introduce a novel approach for long context summarisation, highlight-guided generation.<n>Our framework applies self-planning methods to identify important content and then generates a summary conditioned on the plan.
• Score: 29.76306977276126
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel approach for long context summarisation, highlight-guided generation, that leverages sentence-level information as a content plan to improve the traceability and faithfulness of generated summaries. Our framework applies self-planning methods to identify important content and then generates a summary conditioned on the plan. We explore both an end-to-end and two-stage variants of the approach, finding that the two-stage pipeline performs better on long and information-dense documents. Experiments on long-form summarisation datasets demonstrate that our method consistently improves factual consistency while preserving relevance and overall quality. On GovReport, our best approach has improved ROUGE-L by 4.1 points and achieves about 35% gains in SummaC scores. Qualitative analysis shows that highlight-guided summarisation helps preserve important details, leading to more accurate and insightful summaries across domains.

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