The structural and content aspects of abstracts versus bodies of full text journal articles are different

Abstract

BackgroundAn increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research.ResultsWe examined the structural and linguistic aspects of abstracts and bodies of full text articles, the performance of text mining tools on both, and the distribution of a variety of semantic classes of named entities between them. We found marked structural differences, with longer sentences in the article bodies and much heavier use of parenthesized material in the bodies than in the abstracts. We found content differences with respect to linguistic features. Three out of four of the linguistic features that we examined were statistically significantly differently distributed between the two genres. We also found content differences with respect to the distribution of semantic features. There were significantly different densities per thousand words for three out of four semantic classes, and clear differences in the extent to which they appeared in the two genres. With respect to the performance of text mining tools, we found that a mutation finder performed equally well in both genres, but that a wide variety of gene mention systems performed much worse on article bodies than they did on abstracts. POS tagging was also more accurate in abstracts than in article bodies.ConclusionsAspects of structure and content differ markedly between article abstracts and article bodies. A number of these differences may pose problems as the text mining field moves more into the area of processing full-text articles. However, these differences also present a number of opportunities for the extraction of data types, particularly that found in parenthesized text, that is present in article bodies but not in article abstracts.

Modeling intra-textual variation with entropy and surprisal: topical vs. stylistic patterns

“…Further, there is work on intra-textual variation within the BioNLP community, motivated by the need to extract biomedical knowledge not only from abstracts, but also from full-text articles (Cohen et al, 2010). Besides the use of a pre-defined linguistic feature set, in BioNLP also ontologies are widely employed.…”

Barriers to Open Access Publishing: Views from the Library Literature

“…Some journals contained very structured abstracts while others only provided a single sentence or did not state the purpose and/or conclusion of the study. Other investigations have also shown that when using text mining methods, abstracts have different structural and content characteristics from article bodies even when the abstracts are similarly structured [49,50].…”

Text Mining of Protein Phosphorylation Information Using a Generalizable Rule-Based Approach

“…More recently, the system has also been used to feed information to another text mining system, eFIP, to mine effects of phosphorylation events reported in literature [4,35]. To further enhance the utility of RLIMS-P, we currently aim at making the system generalizable for other PTM types and also applicable to full-text articles, which provides richer biological knowledge beyond abstracts [8].…”