| --- |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - en |
| - multilingual |
| tags: |
| - abstract-detection |
| - scientific-text |
| - quality-filtering |
| - pubverse |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # Abstract Archon Training Data |
|
|
| Binary classification dataset for detecting whether a text is a real scientific research abstract or non-abstract content (figure captions, supplementary material references, author bylines, journal metadata, HTML artifacts, taxonomy stubs). |
|
|
| ## Dataset Details |
|
|
| - **Positive examples (label=1)**: 2,000 real abstracts randomly sampled from a 198M publication database (publications not flagged by any quality filter, length >= 200 characters) |
| - **Negative examples (label=0)**: ~2,000 non-abstract texts from various garbage categories: |
| - `figure_table_caption`: Figure and table captions misidentified as abstracts |
| - `journal_article_scrape`: Scraped article metadata (titles, access info, author lists) |
| - `html_heavy_text`: Content with substantial HTML markup |
| - `author_byline`: Author affiliations and bylines |
| - `moesm_title`: Electronic supplementary material descriptions |
| - `supplementary_content`: Supplementary material references |
| - `taxonomy_stub`: Taxonomic database stubs |
|
|
| ## Format |
|
|
| NDJSON with fields: |
| - `text`: First 500 characters of the abstract field |
| - `label`: 1 (real abstract) or 0 (garbage/non-abstract) |
| - `source`: Category label (e.g., `positive_real_abstract`, `negative_figure_table_caption`) |
| - `source_id`: OpenAlex-style source identifier |
|
|
| ## Data Curation |
|
|
| Negatives were manually curated to remove misclassified entries. The `html_heavy` category was entirely removed after review showed nearly all entries were real abstracts with minor HTML entity artifacts. The `supplementary_content` category was filtered to retain only entries that begin with supplementary material indicators (e.g., "Figure S1", "Additional file", "Supplementary dataset"). |
|
|
| ## Intended Use |
|
|
| Training a lightweight binary classifier (Potion-32M embeddings + LogisticRegression) as a quality gate for large-scale scientific publication databases. The classifier identifies non-abstract text that has been incorrectly stored in abstract fields. |
|
|
| ## Source |
|
|
| Sampled from a PostgreSQL database of ~198M publications aggregated from OpenAlex, PubMed, arXiv, bioRxiv, and medRxiv. Part of the [PubVerse](https://github.com/jimnoneill/pubverse) project. |
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|
