Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances—sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances. 1. Introduction Text mining research in biosciences is concerned with how to extract biologically interesting information from journal articles and other written documents. To date, much of biomedical text processing has been performed on titles, abstracts, and other metadata available for journal articles in PubMed1, as opposed to using full text. While the advantages of full text compared to abstracts have been widely recognized [1–5], until relatively recently, full text was rarely available online, and intellectual property constraints remain even to the present day. These latter constraints are loosening as open access (OA) publications are gaining popularity and online full text is gradually becoming the norm. This trend started in October 2006, when the Wellcome Trust2, a major UK funding body, changed the conditions of grants, requiring that “research papers partly or wholly funded by the Wellcome Trust must be made freely accessible via PubMed Central3 (PMC) (or UK PubMed Central once established) as soon as possible, and in any event no later than six months after publication” [6]. Canadian Institutes of Health Research followed, as did the National Institute of Health (NIH) in the USA in April 2008.4 Moreover, many publishers founded and promoted OA initiatives, namely, BioMed Central5 (BMC) and the Public Library of Science6
Our study also helps answer the question: what abstract claims are not (strongly) supported by the full text? We hypothesize that these would be those claims that are cited very infrequently or not cited at all, but a separate study is required to answer this question.
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Note that here we assume that peers base their citations on full text and not only on the abstract. While this is a strong assumption, we believe that it generally holds in the research community. Our previous studies have shown that biomedical researchers like to verify reported results, for example, by looking at the methods that were used and by exploring the images and the tables in the full text. This has also motivated us to create a specialized search engine, the BioText Search Engine (http://biosearch.berkeley.edu/), for searching the figures and tables contained in open access journals, which is described in [54, 55].
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The data on the analysis considering the extended tree IDs can be found in the supplementary material available online at http://dx.doi.org/10.1155/2012/750214. The majority of results discussed in this paper refer to higher MeSH level annotation representing broader entities and concepts.
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