  Scientific innovation relies on detailed workflows, which include critical
steps such as analyzing literature, generating ideas, validating these ideas,
interpreting results, and inspiring follow-up research. However, scientific
publications that document these workflows are extensive and unstructured. This
makes it difficult for both human researchers and AI systems to effectively
navigate and explore the space of scientific innovation. To address this issue,
we introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization
of Scientific Workflows. MASSW includes more than 152,000 peer-reviewed
publications from 17 leading computer science conferences spanning the past 50
years. Using Large Language Models (LLMs), we 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. We 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. Our dataset is
openly available at \url{https://github.com/xingjian-zhang/massw}.
