  The rapid growth of scientific literature has made it difficult for the
researchers to quickly learn about the developments in their respective fields.
Scientific document summarization addresses this challenge by providing
summaries of the important contributions of scientific papers. We present a
framework for scientific summarization which takes advantage of the citations
and the scientific discourse structure. Citation texts often lack the evidence
and context to support the content of the cited paper and are even sometimes
inaccurate. We first address the problem of inaccuracy of the citation texts by
finding the relevant context from the cited paper. We propose three approaches
for contextualizing citations which are based on query reformulation, word
embeddings, and supervised learning. We then train a model to identify the
discourse facets for each citation. We finally propose a method for summarizing
scientific papers by leveraging the faceted citations and their corresponding
contexts. We evaluate our proposed method on two scientific summarization
datasets in the biomedical and computational linguistics domains. Extensive
evaluation results show that our methods can improve over the state of the art
by large margins.
