The Rich Get Relevant: Neuro-Symbolic Diversity-Inducing Ranking on Semantic Graphs for Personalized Multi-Document Summarization

Query-focused multi-document summarization (MDS) underpins many generative-AI applications (automatic presentations, video and meeting summarization, descriptive question answering, personalized news digests). Yet feeding a long-context LLM raw multi-source text is unreliable: it triggers "lost-in-the-middle" behavior, cross-source redundancy, and hallucination, while classical graph rankers are "feature-blind" and cannot personalize to user intent. We propose RICH, a lightweight (10^5–10^6-parameter) neuro-symbolic ranker that hands the LLM a structured ranked list of (topic, evidence, score) tuples instead of raw text. RICH extracts intent-filtered atomic topics, organizes them in a hierarchical topic–document graph, and runs a parameter-free Recurrent GNN with intent-aware MLP priors and Graph-Attention transitions that scores each topic by jointly balancing relevance and diversity. It embeds the discrete DivRank random walk inside a differentiable model and offers three simple guarantees: RICH is a differentiable form of Pointwise DivRank; its recurrence is a stable damped walk on the simplex with a score floor and a fixed point (fast convergence measured on all evaluation graphs); and its hierarchical prior gives every source document a positive score floor. Across four DiverseSumm setups and an event-level held-out split with disjoint train/test events, RICH leads strong LLM-prompting and classical content-selection baselines on coverage and diversity, with the largest and statistically significant gains in the query-focused settings. The gains transfer zero-shot to Multi-News and WCEP, and a human study confirms the reliability of the evaluation protocol. RICH adds negligible inference overhead ( 0.3% of end-to-end latency) on top of the LLM calls that every baseline also makes.