Large language model (LLM) approaches to tabular summarization rely on extensive prompt engineering, decomposition pipelines, or entity-level intermediate representations to achieve strong performance. While effective, these strategies are computationally expensive and offer limited insight into how well models maintain state over long, evolving narratives. We introduce SporTabSet, a diagnostic benchmark for long-context tabular summarization across two complementary sports domains that require tracking multiple entities and aggregating statistics under domain-specific rules. Using SporTabSet, we systematically evaluate decomposition-based strategies across several long context LLMs. Results show that although decomposition substantially improves accuracy and numerical fidelity, gains stem mainly from dissecting multi-entity interference rather than improved local arithmetic. Robustness experiments further reveal high sensitivity to surface-level cues with structured failures, including hallucination, omission, and role confusion. Together, these findings identify consistent multi-entity memory as a key bottleneck in long-context table generation, motivating diagnostic evaluation as a prerequisite for scalable, efficient, and reliable tabular summarization models.

Many analytical questions over long narratives are structured even when their source documents are not. Answering them requires identifying relevant events, inducing a query-specific schema, grounding records in dispersed evidence, and computing the values of an answer table. We introduce Q-STR, a cricket-domain benchmark and diagnostic for this query-driven text-to-table problem. We construct Q-STR from ball-by-ball match narratives using executable SQL templates and release two complementary splits: a 4,256-example coverage split for breadth and a 3,467-example controlled split spanning 348 reasoning templates for within-template diagnosis. Factorized metrics distinguish recovery of the answer-table structure from fidelity of its cell values. Across four language models and seven prompting strategies, models often recover plausible table structure while failing at grounded aggregation. Gemini-2.5-Flash is strongest overall, but even its best configuration reaches only 28.56 exact table match. For open-weight models, allocating output tokens to row-local evidence and computation improves cell fidelity more reliably than multi-call planning or self-consistency. Controlled comparisons further show that event identification and numeric fidelity are only partially coupled: execution pressure is the most consistent difficulty driver, while increasing output pressure shifts errors toward undercounting. Q-STR is deliberately a cricket diagnostic rather than a general benchmark of all narrative reasoning. Its single-domain scope, model-assisted construction, and absence of an executable code-generation baseline limit broader conclusions, but its controlled design exposes where structured reasoning from long narratives breaks down.