@inproceedings{ting-etal-2025-astromlab,
    title = "{A}stro{ML}ab 5: Structured Summaries and Concept Extraction for 400,000 Astrophysics Papers",
    author = "Ting, Yuan-Sen  and
      Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Nguyen, Tuan Dung  and
      Pan, Rui  and
      Sun, Zechang  and
      de Haan, Tijmen",
    editor = "Accomazzi, Alberto  and
      Ghosal, Tirthankar  and
      Grezes, Felix  and
      Lockhart, Kelly",
    booktitle = "Proceedings of the Third Workshop for Artificial Intelligence for Scientific Publications",
    month = dec,
    year = "2025",
    address = "Mumbai, India and virtual",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.wasp-main.19/",
    pages = "170--185",
    ISBN = "979-8-89176-310-4",
    abstract = "We present a dataset of 408,590 astrophysics papers from arXiv (astro-ph), spanning 1992 through July 2025. Each paper has been processed through a multi-stage pipeline to produce: (1) structured summaries organized into six semantic sections (Background, Motivation, Methodology, Results, Interpretation, Implication), and (2) concept extraction yielding 9,999 unique concepts with detailed descriptions. The dataset contains 3.8 million paper-concept associations and includes semantic embeddings for all concepts. Comparison with traditional ADS keywords reveals that the concepts provide denser coverage and more uniform distribution, while analysis of embedding space structure demonstrates that concepts are semantically dispersed within papers{---}enabling discovery through multiple diverse entry points. Concept vocabulary and embeddings are publicly released at https://github.com/tingyuansen/astro-ph{\_}knowledge{\_}graph."
}