A Comparative Evaluation of End-to-End and Pipeline Approaches for Summarisation
Published in Proceedings of the 1st Symposium on Natural Language Generation Evaluations, 2026
We describe and evaluate two different architectures for creating book highlights from unstructured data. Given the prevalence of large language models, we examine whether a pipeline-based approach with intermediate steps for text generation is still necessary and whether it continues to offer any benefits over an end-to-end approach. Our comparative evaluations using LLM-as-a-judge across multiple models with different parameter sizes and generation scenarios show that highlights generated by the end-to-end approach are preferred. However, there is a slight but consistent increase in faithfulness for the pipeline-generated highlights when generating at a thematic level. Additionally, our analysis across multiple models shows that while larger models are more faithful, the degree of faithfulness increases when they are used with a pipeline architecture. The findings from our work indicate that whilst there is comparability between the two approaches, the greater faithfulness, controllability, and observability of pipeline-based approaches offer tangible benefits in applied settings.