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AutoDDG: Automated Dataset Description Generation using Large Language Models

Summary: AutoDDG generates dataset descriptions for tabular data by combining data-driven content summarization with LLM-based semantic enrichment, targeting missing/inaccurate metadata in data lakes/open portals. Proposes a multi-faceted evaluation (retrieval, reference-based, reference-free, human) and shows improved dataset search/retrieval at scale. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID
7643
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,431 | 28.44%
DOI
10.1145/3786626

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BibTeX Citation

@inproceedings{zhang_sigmod26,
        title = {{AutoDDG: Automated Dataset Description Generation using Large Language Models}},
        author = {Zhang, Haoxiang and Liu, Yurong and Santos, Aécio and Hung, Wei-Lun (Allen) and Freire, Juliana},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786626},
        url = {https://dl.acm.org/doi/10.1145/3786626},
        year = {2026}
}

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