During the last decade the amount of scientific information
available on-line increased at an unprecedented rate. Recent estimates reported that a new paper is published every 20 seconds.
As a consequence, nowadays researchers are overwhelmed by an
enormous and continuously growing number of articles to consider
when they perform any activity that requires a careful and comprehensive
assessment of scientific literature, like the exploration of
advances in specific topics, peer reviewing, writing and
evaluation of proposals.
Natural Language Processing Technology represents a key enabling factor in providing scientists with intelligent patterns to access to scientific information. Extracting information from scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intelligent knowledge access and question answering. Summarization techniques can reduce the size of long papers to their essential content or automatically generate state-of-the-art-reviews. Paraphrase or textual entailment techniques can contribute to the identification of relations across different scientific textual sources. This tutorial provides an overview of the most relevant tasks related to the processing of scientific documents, including but not limited to the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction and summarization.