Distribution of information in biomedical abstracts and full-text publications

Bioinformatics. 2004 Nov 1;20(16):2597-604. doi: 10.1093/bioinformatics/bth291. Epub 2004 May 6.


Motivation: Full-text documents potentially hold more information than their abstracts, but require more resources for processing. We investigated the added value of full text over abstracts in terms of information content and occurrences of gene symbol--gene name combinations that can resolve gene-symbol ambiguity.

Results: We analyzed a set of 3902 biomedical full-text articles. Different keyword measures indicate that information density is highest in abstracts, but that the information coverage in full texts is much greater than in abstracts. Analysis of five different standard sections of articles shows that the highest information coverage is located in the results section. Still, 30-40% of the information mentioned in each section is unique to that section. Only 30% of the gene symbols in the abstract are accompanied by their corresponding names, and a further 8% of the gene names are found in the full text. In the full text, only 18% of the gene symbols are accompanied by their gene names.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Abstracting and Indexing / methods*
  • Abstracting and Indexing / standards*
  • Bibliometrics
  • Biomedical Research / statistics & numerical data*
  • Genes*
  • Information Dissemination / methods
  • Information Storage and Retrieval / methods*
  • MEDLINE / statistics & numerical data
  • Natural Language Processing*
  • Periodicals as Topic / statistics & numerical data*
  • Terminology as Topic