What they found
The authors propose ReSUM, a system where several language models work together. A planner directs one set of agents that find relevant parts of a meeting and another set that write the summary. The authors train the retrieval and summary agents by distilling reasoning from a teacher model. Distilling means teaching a model to copy the way a stronger model reasons. The abstract says this makes SI systems more robust on long inputs, cuts irrelevant searching, and gives more faithful and complete summaries. It says experiments show consistent gains on query-focused meeting summarization benchmarks, but it gives no numbers or named benchmarks.
Why it matters
Query-focused summarization means answering a specific question about a long document, such as a meeting transcript. Meetings are hard because speakers interrupt, transcripts are long, and key facts are sparse. The abstract notes that even advanced models can omit information or make things up. Splitting the job into retrieval, evidence and writing steps is one way to cut those errors.
How it changes the race
This is an incremental, task-specific result. It supports the idea that planned, modular SI agents can beat single-pass methods on long inputs, but the abstract gives too little detail to judge how large the gain is.
Safety and control
The paper targets hallucination and information omission in summaries, which bear on how far people can trust SI output. The abstract gives no detail on how faithfulness was measured.
The abstract, by the authors
LLMs excel at language tasks, but long-meeting QFS remains difficult due to complex speaker dynamics, sparse key information, and limited data. Meetings involve disfluencies, long transcripts and complex discourse. QFS have high risk of information omission, hallucination, even for advanced models such as GPT-4-mini and Gemini. Existing end-to-end methods compress retrieval, evidence extraction, and summary generation into a single pass, failing to handle the complex reasoning required for targeted meeting summaries. We propose ReSUM, a modular multi-LLM framework coordinated by a planner that orchestrates retrieval and summarization agents. By distilling teacher reasoning into query- and plan-conditioned retrievers and summarizers, ReSUM enhances long-context robustness, minimizes irrelevant exploration, and generates more faithful and comprehensive query-focused summaries. Experiments show consistent gains on query-focused meeting summarization benchmarks.
Quoted from arXiv:2611.09349. The research and its words belong to its authors.