Summary of Strum-llm: Attributed and Structured Contrastive Summarization, by Beliz Gunel et al.
STRUM-LLM: Attributed and Structured Contrastive Summarization
by Beliz Gunel, James B. Wendt, Jing Xie, Yichao Zhou, Nguyen Vo, Zachary Fisher, Sandeep Tata
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed STRUM-LLM technique addresses decision-making challenges by generating contrastive summaries highlighting key differences between two options (A vs B). By identifying helpful contrasts, the model identifies specific attributes that significantly differ between options and are likely to influence user decisions. This domain-agnostic approach requires no human-labeled data or fixed attribute list as supervision. STRUM-LLM attributes extractions back to input sources with textual evidence, processing input sources of varying lengths. The Distilled version boasts 100x more throughput than comparable models while being 10x smaller. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary STRUM-LLM helps people make decisions by comparing two options (A vs B). It finds the most important differences between them and shows why they might matter to you. This tool is special because it works without needing a lot of labeled data or a fixed list of things to compare. It even tells you where it got its information from! STRUM-LLM can handle different amounts of text and is really good at finding the most important parts. |
