  We present a methodology combining surface NLP and Machine Learning
techniques for ranking asbtracts and generating summaries based on annotated
corpora. The corpora were annotated with meta-semantic tags indicating the
category of information a sentence is bearing (objective, findings, newthing,
hypothesis, conclusion, future work, related work). The annotated corpus is fed
into an automatic summarizer for query-oriented abstract ranking and multi-
abstract summarization. To adapt the summarizer to these two tasks, two novel
weighting functions were devised in order to take into account the distribution
of the tags in the corpus. Results, although still preliminary, are encouraging
us to pursue this line of work and find better ways of building IR systems that
can take into account semantic annotations in a corpus.
