Dynamic summarization of bibliographic-based data

Abstract

BackgroundTraditional information retrieval techniques typically return excessive output when directed at large bibliographic databases. Natural Language Processing applications strive to extract salient content from the excessive data. Semantic MEDLINE, a National Library of Medicine (NLM) natural language processing application, highlights relevant information in PubMed data. However, Semantic MEDLINE implements manually coded schemas, accommodating few information needs. Currently, there are only five such schemas, while many more would be needed to realistically accommodate all potential users. The aim of this project was to develop and evaluate a statistical algorithm that automatically identifies relevant bibliographic data; the new algorithm could be incorporated into a dynamic schema to accommodate various information needs in Semantic MEDLINE, and eliminate the need for multiple schemas.MethodsWe developed a flexible algorithm named Combo that combines three statistical metrics, the Kullback-Leibler Divergence (KLD), Riloff's RlogF metric (RlogF), and a new metric called PredScal, to automatically identify salient data in bibliographic text. We downloaded citations from a PubMed search query addressing the genetic etiology of bladder cancer. The citations were processed with SemRep, an NLM rule-based application that produces semantic predications. SemRep output was processed by Combo, in addition to the standard Semantic MEDLINE genetics schema and independently by the two individual KLD and RlogF metrics. We evaluated each summarization method using an existing reference standard within the task-based context of genetic database curation.ResultsCombo asserted 74 genetic entities implicated in bladder cancer development, whereas the traditional schema asserted 10 genetic entities; the KLD and RlogF metrics individually asserted 77 and 69 genetic entities, respectively. Combo achieved 61% recall and 81% precision, with an F-score of 0.69. The traditional schema achieved 23% recall and 100% precision, with an F-score of 0.37. The KLD metric achieved 61% recall, 70% precision, with an F-score of 0.65. The RlogF metric achieved 61% recall, 72% precision, with an F-score of 0.66.ConclusionsSemantic MEDLINE summarization using the new Combo algorithm outperformed a conventional summarization schema in a genetic database curation task. It potentially could streamline information acquisition for other needs without having to hand-build multiple saliency schemas.

Rethinking information delivery: using a natural language processing application for point-of-care data discovery

“…This Perl application provided adequate computing speeds for this project; however, to increase speed, the software could also be coded with a compiled language like Java. A locally accessible database of SemRep output for several years' worth of MEDLINE data is also needed (for a more detailed description of how the system works, please see Workman and Hurdle [7]). Currently, there is not a publicly accessible, Combo-enhanced Semantic MED-LINE web portal; however, this paper provides a brief description of how a library could customize its own application to serve its particular clients.…”

“…Combo [7], can simplify MEDLINE results for many information needs. The user activates the Semantic MEDLINE application by submitting a search query expressing an information need to PubMed.…”

“…They wanted to determine if this approach could retrieve recommended preventive interventions found in a commercial, manually annotated product. Prior efforts in applying Semantic MEDLINE, with the Combo algorithm, in a simulated database curation task to identify information relevant to genetic disease etiology were successful [7]. The authors wanted to evaluate the system in a simulated clinical decision support task.…”

“…First, in evaluating Combo-enhanced Semantic MEDLINE for other related projects (addressing genetic disease etiology [7] and drug treatment), information retrieval for text summarization was based on a single disease topic, paired with a subheading-type concept, while drawing on all citations in the database (instead of selected intricate subsets). This provided some standardization across all projects, thus facilitating the eventual comparison of all results.…”

“…Combo [7] analyzes predicate frequencies using an adaptation of the Kullback-Leibler Divergence [11] and measures the strength of predicate/semantic type pairings with Riloff's RlogF metric [12] and PredScal, a scaling metric developed for the Combo algorithm. Prior to this approach, summarization depended on conventional, static applications, called schemas, which are limited to specified ''subject_predicate_object'' patterns.…”