README.md

metadata

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 project.