In the current context of scientific information overload in which new knowledge is created at a rapid pace, we propose to develop text summarization services for automatically identifying the most important information of a research article. The work will be based on an adaptation of our current scientific text mining and summarization technology at our  LaSTUS/TALN lab. The summarization system will apply a natural language processing pipeline for deep analysis of scientific documents and compute a series of sentence relevance features based on the results of text analysis. 

OpenMinTeD infrastructure link.