@inproceedings{du-etal-2025-enhancing,
title = "Enhancing Long Document Long Form Summarisation with Self-Planning",
author = "Du, Xiaotang and
Saxena, Rohit and
Perez-Beltrachini, Laura and
Minervini, Pasquale and
Titov, Ivan",
editor = "Inui, Kentaro and
Sakti, Sakriani and
Wang, Haofen and
Wong, Derek F. and
Bhattacharyya, Pushpak and
Banerjee, Biplab and
Ekbal, Asif and
Chakraborty, Tanmoy and
Singh, Dhirendra Pratap",
booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
month = dec,
year = "2025",
address = "Mumbai, India",
publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
url = "https://aclanthology.org/2025.ijcnlp-short.27/",
pages = "317--332",
ISBN = "979-8-89176-299-2",
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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%0 Conference Proceedings
%T Enhancing Long Document Long Form Summarisation with Self-Planning
%A Du, Xiaotang
%A Saxena, Rohit
%A Perez-Beltrachini, Laura
%A Minervini, Pasquale
%A Titov, Ivan
%Y Inui, Kentaro
%Y Sakti, Sakriani
%Y Wang, Haofen
%Y Wong, Derek F.
%Y Bhattacharyya, Pushpak
%Y Banerjee, Biplab
%Y Ekbal, Asif
%Y Chakraborty, Tanmoy
%Y Singh, Dhirendra Pratap
%S Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics
%D 2025
%8 December
%I The Asian Federation of Natural Language Processing and The Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-299-2
%F du-etal-2025-enhancing
%X 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.
%U https://aclanthology.org/2025.ijcnlp-short.27/
%P 317-332
Markdown (Informal)
[Enhancing Long Document Long Form Summarisation with Self-Planning](https://aclanthology.org/2025.ijcnlp-short.27/) (Du et al., IJCNLP-AACL 2025)
ACL
- Xiaotang Du, Rohit Saxena, Laura Perez-Beltrachini, Pasquale Minervini, and Ivan Titov. 2025. Enhancing Long Document Long Form Summarisation with Self-Planning. In Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, pages 317–332, Mumbai, India. The Asian Federation of Natural Language Processing and The Association for Computational Linguistics.