Abstract

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.