[Document Summarization for AI-based Post-Editing](https://aclanthology.org/2026.amta-research.11.pdf)
[Vera Senderowicz Guerra](/people/vera-senderowicz-guerra/unverified/),
[Dimitrios Pavlou](/people/dimitrios-pavlou/unverified/),
[Peter Bourgonje](/people/peter-bourgonje/unverified/),
[Olesia Khrapunova](/people/olesia-khrapunova/unverified/),
[Konstantinos Karageorgos](/people/konstantinos-karageorgos/unverified/),
[Aaron Schliem](/people/aaron-schliem/unverified/)
Abstract
Post-Editing (PE) is typically performed on isolated segments or small batches, without access to broader document context. In this paper, we investigate whether pre-generated, document-level summaries can improve PE quality. Using a purpose-built summarization prompt evaluated across nine LLMs from OpenAI and Google, we select two models with contrasting summary styles for downstream experiments on 448 documents covering 37 target locales and 13 content domains. Summaries generated by gemini-2.5-flash-lite, which are directive and domain-specific, yield gains in edit distance and modest gains in COMET, whereas those generated by GPT-4o, which tend to be more generic and descriptive, degrade performance across most metrics. The positive effect appears most pronounced in terminologically dense domains and lower-resource locales. A qualitative analysis shows that improvements arise when summaries provide specific, actionable guidance on terminology, domain conventions, and style, and that performance decreases when summaries are underspecified or conflicting. These findings suggest that summary specificity and actionability, rather than the mere addition of context, determine whether document-level information benefits post-editing.- Anthology ID:
- 2026.amta-research.11
- Volume:
[Proceedings of the 17th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track)](/volumes/2026.amta-research/)- Month:
- August
- Year:
- 2026
- Address:
- Québec City, Canada
- Editors:
[Eleftheria Briakou](/people/eleftheria-briakou/unverified/),[Jeremy Gwinnup](/people/jeremy-gwinnup/),[Shivali Goel](/people/shivali-goel/unverified/)- Venue:
[AMTA](/venues/amta/)- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 174–185
- Language:
- URL:
[https://aclanthology.org/2026.amta-research.11/](https://aclanthology.org/2026.amta-research.11/)- DOI:
- Cite (ACL):
- Vera Senderowicz Guerra, Dimitrios Pavlou, Peter Bourgonje, Olesia Khrapunova, Konstantinos Karageorgos, and Aaron Schliem. 2026. Document Summarization for AI-based Post-Editing. InProceedings of the 17th Conference of the Association for Machine Translation in the Americas (Volume 1: Research Track), pages 174–185, Québec City, Canada. Association for Machine Translation in the Americas. - Cite (Informal):
[Document Summarization for AI-based Post-Editing](https://aclanthology.org/2026.amta-research.11/)(Guerra et al., AMTA 2026)- PDF:
[https://aclanthology.org/2026.amta-research.11.pdf](https://aclanthology.org/2026.amta-research.11.pdf)