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)
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Authors
- 1. Haoxiang Zhang (New York University)
- 2. Yurong Liu (New York University)
- 3. Aécio Santos (New York University)
- 4. Wei-Lun (Allen) Hung (New York University)
- 5. Juliana Freire (New York University)
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}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,894 | MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery | 2026 | VLDB | 4.9793485e-05 |
| 11,038 | Semantic Data Systems: From Data Management to Data Understanding | 2026 | VLDB | 4.9793485e-05 |
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