Enhancing Long Document Long Form Summarisation with Self-Planning

2025-12-18

AI Summary

To address factual inconsistency, information loss, and poor traceability in long-document summarization, this paper proposes a sentence-level highlighting-guided self-planning generation framework. First, it identifies salient sentences via importance modeling and generates a traceable content plan; subsequently, summary generation is conditioned on this plan, effectively decoupling content selection from surface realization. This novel paradigm significantly enhances summary faithfulness and fine-grained detail retention. On the GovReport benchmark, our approach achieves a +4.1-point improvement in ROUGE-L and a 35% gain in SummaC score. Qualitative analysis confirms more complete preservation of critical details, as well as improved cross-domain accuracy and analytical depth in generated summaries.

Technology Category

• Natural Language Processing: Summarization
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Improves factual consistency in long document summarization.
• Enhances traceability and faithfulness of generated summaries.
• Preserves important details for accurate and insightful summaries.

Innovation

• Self-planning identifies important content for summaries
• Highlight-guided generation uses sentence-level information as plan
• Two-stage pipeline improves factual consistency in long documents