Building on the work of Ruch et al. ( 2003 ) in the same domain , we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models ( HMMs ) ; cfXXX ( Barzilay and Lee , 2004 ) .
catching the drift probabilistic content models with applications to generation and summarization
Regina Barzilay Lillian Lee
McKnight and Srinivasan ( 2003 ) have previously examined the task of categorizing sentences in medical abstracts using supervised discriminative machine learning techniques . Building on the work of Ruch et al. ( 2003 ) in the same domain , we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models ( HMMs ) ; cfXXX ( Barzilay and Lee , 2004 ) . Although our results were not obtained from the same exact collection as those used by authors of these two previous studies , comparable experiments suggest that our techniques are competitive in terms of performance , and may offer additional advantages as well .
Building on the work of Ruch et al. ( 2003 ) in the same domain , we present a generative approach that attempts to directly model the discourse structure of MEDLINE abstracts using Hidden Markov Models ( HMMs ) ; cfXXX ( @@CITATION ) .