Exploiting Textual and Citation Information to Identify and Summarize Influential Publications

Given a group of publications, we investigate the prob- lem of identifying the papers with the most impact on others. We refer to these papers as influential in the sense that they introduce new concepts and language that will affect how future articles are written. In this pa- per we propose weighted PageRank algorithm that uses textual information from articles and information from citation graph to rank the impact of publications, then we automatically summarize these publications and ex- tract important keywords. We show that using our algo- rithm outperforms default citation-based techniques in ranking influential papers (those which won best paper award) with no less than 2% in F1-score and NDCG. We also show that our algorithm outperforms previous graph-based keyword extraction techniques with no less than 1.5% in F1-score.