Quantitative impact of ordering and grouping on semantic aggregation quality

Determine how input document ordering and semantic grouping strategies (e.g., clustering-based partitioning) influence the quality of summarization produced by LOTUS’s sem_agg operator, and provide quantitative metrics and empirical evaluations that compare naive ordering against semantic-cluster-based partitioning for multi-document aggregation tasks.

Background

The paper introduces LOTUS’s sem_agg operator to perform semantic aggregations, such as many-to-one summarization, over large collections of documents. The authors observe that aggregation quality is sensitive to how input documents are ordered and grouped within LLM contexts, showing contrasting qualitative outcomes between naive ordering and clustering-based partitioning.

Specifically, they present qualitative evidence that clustering documents by semantic similarity before aggregation yields summaries with greater cohesion and higher-level thematic abstraction, whereas naive ordering tends to produce less coherent results focused on low-level details. The authors explicitly leave a quantitative assessment of these effects to future work, motivating the need to establish metrics and systematic experiments that characterize and measure the impact of ordering and grouping on sem_agg outcomes.