Citation-based Extraction of Core Contents from Biomedical Articles Rey-Long Liu Dept. of Medical Informatics Tzu Chi University Taiwan

Outline Background Problem definition The proposed technique: CoreCE Empirical evaluation Conclusion

Background

Core Contents of Biomedical Articles Core contents of a scholarly article a are the textual contents about Research goal of a Research background of a Research conclusion of a

Why Extraction of the Core Contents? Indexing of the articles Mining & analysis of highly related evidence Keyword-based search of the articles Search engines often work by keyword input But the extraction is challenging Core content of an article a may be expressed in different ways and scattered in a.

Selected by biomedical experts for <erythropoietin, anemia>  They are highly related to each other Recommended by PubMed, but not highly related to <erythropoietin, anemia> 6

Problem Definition

Goal & Contribution Goal Contribution Given a scholarly article a, extract the core content of a Contribution Developing a technique CoreCE (Core Content Extractor) that extracts the core content based on how the article cites references  citation-based extraction

Related Work Extraction of citation links In-link citations (how article a is cited by others) Out-link citations (how article a cites others)  Cannot support keyword-based retrieval Extraction of textual contents Certain important parts (e.g., titles and abstracts) Certain terms with higher weights (e.g., TFIDF weight)  But core content of an article a may be expressed in different ways and scattered in a

The Proposed Technique: CoreCE

Basic Definitions

Interesting Ideas of CoreCE Core content of article a is extracted from Title and abstract of a, AND Titles of the references cited by a Term frequency of a term t is amplified if t appears in citation passages of the references cited by a The core content is represented by plain text Applicable to keyword-based indexing & retrieval

Empirical Evaluation

The data Two sets of articles Highly related biomedical articles: For each gene-disease pair <g,d>, collect the biomedical articles that biomedical experts selected to annotate the pair (noted by DisGeNET) Near-miss biomedical articles (Non-highly related articles): For each gene-disease pair <g,d>, collect articles using two queries: “g NOT d” and “d NOT g”

Data statistics 53 gene-disease pairs 9,876 articles, including 53 targets + 9,823 candidates 435,786 out-link references

The Systems to Be Evaluated (1) Title Only (2) Abstract Only (3) Title+Abstract (4) Title+Abstract+ReferenceTitles (5) Whole Article (including the main body) (6) CoreCE

The Underlying Inter-Article Similarity Measure One of the state-of-the-art measures:

Evaluation Criterion MAP (Mean Average Precision) If a system can rank higher those articles that are highly related to r, average precision (AvgP) for the gene-disease pair will be higher MAP is simply the average of the AvgP values for all gene-disease pairs

Average P@X If those articles that are highly related to r, are ranked at top-X position, P@X for the gene-disease pair will be higher Average P@X is simply the average of the P@X values for all gene-disease pairs

Result With the core contents extracted by CoreCE, the system performs significantly better in ranking highly related articles

CoreCE helps to rank highly related articles at top positions (top-1 and top-3) for a higher percentage of the testes

CoreCE performs better when the size is set to 5, however the performance differences are not statistically significant

Conclusion

Core content of a scholarly article a is The fundamental basis for the indexing, retrieval, and analysis of scientific literature, BUT Scattered in a and expressed with different terms We develop CoreCE that Extracts the core content based on titles and citation passages of the references cited by a The idea of CoreCE can be Incorporated as a front-end processor for search engines to properly index scholarly articles