@article{PhysRevE.64.016131,
  author = {Newman, M. E. J.},
  doi = {10.1103/PhysRevE.64.016131},
  interhash = {c2e3ef110ba67dd66249c354725aa680},
  intrahash = {c4ec4bf95bf426882af0061bee863511},
  journal = {Phys. Rev. E},
  month = jun,
  number = 1,
  numpages = {8},
  pages = 016131,
  publisher = {American Physical Society},
  title = {Scientific collaboration networks.  I. Network construction and fundamental results},
  url = {http://link.aps.org/doi/10.1103/PhysRevE.64.016131},
  volume = 64,
  year = 2001
}

@article{Teufel02summarizingscientific,
  abstract = {this paper we argue that scientific articles require a different summarization strategy than, for instance, news articles. We propose a strategy which concentrates on the rhetorical status of statements in the article: Material for summaries is selected in such a way that summaries can highlight the new contribution of the source paper and situate it with respect to earlier work. We provide a gold standard for summaries of this kind consisting of a substantial corpus of conference articles in computational linguistics with human judgements of rhetorical status and relevance. We present several experiments measuring our judges' agreement on these annotations. We also present an algorithm which, on the basis of the annotated training material, selects content and classifies it into a fixed set of seven rhetorical categories. The output of this extraction and classification system can be viewed as a single-document summary in its own right; alternatively, it can be used to generate task-oriented and user-tailored summaries designed to give users an overview of a scientific field.},
  author = {Teufel, Simone and Moens, Marc},
  interhash = {5062ef01775fa6300141a99937d0f1cd},
  intrahash = {7b5e363f72b4351d3afba8f2b369bed6},
  journal = {Computational Linguistics},
  pages = 2002,
  title = {Summarizing Scientific Articles - Experiments with Relevance and Rhetorical Status},
  url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.5593},
  volume = 28,
  year = 2002
}

@inproceedings{conf/vl/GoveDSKD11,
  author = {Gove, Robert and Dunne, Cody and Shneiderman, Ben and Klavans, Judith and Dorr, Bonnie J.},
  booktitle = {VL/HCC},
  crossref = {conf/vl/2011},
  editor = {Costagliola, Gennaro and Ko, Andrew Jensen and Cypher, Allen and Nichols, Jeffrey and Scaffidi, Christopher and Kelleher, Caitlin and Myers, Brad A.},
  ee = {http://dx.doi.org/10.1109/VLHCC.2011.6070403},
  interhash = {9820a691a515e4f1a38f31056320c3a2},
  intrahash = {3f86bd37731ba68da7422834d82b55e3},
  isbn = {978-1-4577-1246-3},
  pages = {217-224},
  publisher = {IEEE},
  title = {Evaluating visual and statistical exploration of scientific literature networks.},
  url = {http://dblp.uni-trier.de/db/conf/vl/vlhcc2011.html#GoveDSKD11},
  year = 2011
}

@article{1742-5468-2007-06-P06010,
  abstract = {To account for strong ageing characteristics of citation networks, we modify the PageRank algorithm by initially distributing random surfers exponentially with age, in favour of more recent publications. The output of this algorithm, which we call CiteRank, is interpreted as approximate traffic to individual publications in a simple model of how researchers find new information. We optimize parameters of our algorithm to achieve the best performance. The results are compared for two rather different citation networks: all American Physical Society publications between 1893 and 2003 and the set of high-energy physics theory (hep-th) preprints. Despite major differences between these two networks, we find that their optimal parameters for the CiteRank algorithm are remarkably similar. The advantages and performance of CiteRank over more conventional methods of ranking publications are discussed.},
  author = {Walker, Dylan and Xie, Huafeng and Yan, Koon-Kiu and Maslov, Sergei},
  interhash = {86853f761733eaea09a273027a6c3c4a},
  intrahash = {ed618f45800255b5a5179d36849cd0b4},
  journal = {Journal of Statistical Mechanics: Theory and Experiment},
  number = 06,
  pages = {P06010},
  title = {Ranking scientific publications using a model of network traffic},
  url = {http://stacks.iop.org/1742-5468/2007/i=06/a=P06010},
  volume = 2007,
  year = 2007
}