Summary of Massw: a New Dataset and Benchmark Tasks For Ai-assisted Scientific Workflows, by Xingjian Zhang et al.
MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows
by Xingjian Zhang, Yutong Xie, Jin Huang, Jinge Ma, Zhaoying Pan, Qijia Liu, Ziyang Xiong, Tolga Ergen, Dongsub Shim, Honglak Lee, Qiaozhu Mei
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper introduces MASSW, a comprehensive text dataset on Multi-Aspect Summarization of Scientific Workflows. The dataset includes over 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. Using Large Language Models (LLMs), the authors automatically extract five core aspects from these publications – context, key idea, method, outcome, and projected impact – which correspond to five key steps in the research workflow. These structured summaries facilitate a variety of downstream tasks and analyses. The quality of the LLM-extracted summaries is validated by comparing them with human annotations. The authors demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset, which make various types of predictions and recommendations along the scientific workflow. MASSW holds significant potential for researchers to create and benchmark new AI methods for optimizing scientific workflows and fostering scientific innovation in the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MASSW is a big database that helps scientists understand how research is done. It takes over 152,000 papers from computer science conferences and makes summaries of each one. These summaries have five main parts: what’s going on, the main idea, how it was done, what happened, and what might happen next. This makes it easier for computers to analyze and learn from the research. The authors checked their summaries against human versions and showed that they’re pretty good. They also gave some examples of things you can do with MASSW. |
Keywords
» Artificial intelligence » Machine learning » Summarization
