Structuralizing biomedical abstracts with discriminative linguistic features

Objective Nearly 75% of the abstracts in MEDLINE papers present in an unstructured format. This study aims to automate the reformatting of unstructured abstracts into the Introduction, Methods, Results, and Discussion (IMRAD) format. The quality of this reformatting relies on the features used in sentence classification. Therefore, we explored the most effective linguistic features in MEDLINE papers. Methods We constructed a feature set consisting of bag of words, linguistic features, grammatical features, and structural features. In order to evaluate the effectiveness, which is the capability of the sentence classification with the features, three datasets from PubMed Central Open Access Subset were selected and constructed: (1) structured abstract (SA) for training, (2) unstructured RCT abstract (UA-1) and (3) unstructured general abstract (UA-2). F-score and accuracy were used to measure the effectiveness on IMRAD section level and the overall classification. Results Adding linguistic features improves the classification of the abstract sentence from 1.2% to 35.8% in terms of accuracy in three abstract datasets. The highest accuracies achieved were 91.7% in SA, 86.3% in UA-1, and 77.9% in UA-2. Linguistic features (dimensions=15) had fewer dimensions than bag-of-words (dimensions= 1541). All representative linguistic features (n-gram and verb phrase, and noun phrase) for each section are identified in our system (available at http://abstract.bike.re.kr). Conclusion Linguistic features can be used to effectively classify sentence with low computation burden in MEDLINE abstract.