title: Using argumentation to extract key sentences from biomedical abstracts.
abstract: OBJECTIVE
  key word assignment has been largely used in MEDLINE to provide an indicative "gist" of the content of articles and to help retrieving biomedical articles. Abstracts are also used for this purpose. However with usually more than 300 words, MEDLINE abstracts can still be regarded as long documents; therefore we design a system to select a unique key sentence. This key sentence must be indicative of the article's content and we assume that abstract's conclusions are good candidates. We design and assess the performance of an automatic key sentence selector, which classifies sentences into four argumentative moves: PURPOSE, METHODS, RESULTS and
  
  
  CONCLUSIONS
  
  
  
  METHODS
  we rely on Bayesian classifiers trained on automatically acquired data. Features representation, selection and weighting are reported and classification effectiveness is evaluated on the four classes using confusion matrices. We also explore the use of simple heuristics to take the position of sentences into account. Recall, precision and F-scores are computed for the CONCLUSION class. For the CONCLUSION class, the F-score reaches 84%. Automatic argumentative classification using Bayesian learners is feasible on MEDLINE abstracts and should help user navigation in such repositories.
authors.affiliation: SIM, University Hospitals of Geneva, Geneva, CH, Switzerland. patrick.ruch@sim.hcuge.ch
authors.forename: Patrick
authors.initials: P
authors.lastname: Ruch
authors.forename: Celia
authors.initials: C
authors.lastname: Boyer
authors.forename: Christine
authors.initials: C
authors.lastname: Chichester
authors.forename: Imad
authors.initials: I
authors.lastname: Tbahriti
authors.forename: Antoine
authors.initials: A
authors.lastname: Geissbühler
authors.forename: Paul
authors.initials: P
authors.lastname: Fabry
authors.forename: Julien
authors.initials: J
authors.lastname: Gobeill
authors.forename: Violaine
authors.initials: V
authors.lastname: Pillet
authors.forename: Dietrich
authors.initials: D
authors.lastname: Rebholz-Schuhmann
authors.forename: Christian
authors.initials: C
authors.lastname: Lovis
authors.forename: Anne-Lise
authors.initials: AL
authors.lastname: Veuthey
mesh_terms: D000043:Abstracting and Indexing; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015706:Bibliometrics; D016247:Information Storage and Retrieval / Q000379:methods*; D046609:Libraries, Digital; D016239:MEDLINE; D009323:Natural Language Processing; D010506:Periodicals as Topic; D009626:Terminology as Topic; D018875:Vocabulary, Controlled