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The amount of scientific papers published every day is daunting andconstantly increasing. Keeping up with literature represents a challenge. Ifone wants to start exploring new topics it is hard to have a big picturewithout reading lots of articles. Furthermore, as one reads through literature,making mental connections is crucial to ask new questions which might lead todiscoveries. In this work, I present a web tool which uses a Text Miningstrategy to transform large collections of unstructured biomedical articlesinto structured data. Generated results give a quick overview on complex topicswhich can possibly suggest not explicitly reported information. In particular,I show two Data Science analyses. First, I present a literature based rarediseases network build using this tool in the hope that it will help clarifysome aspects of these less popular pathologies. Secondly, I show how aliterature based analysis conducted with PubSqueezer results allows to describeknown facts about SARS-CoV-2. In one sentence, data generated with PubSqueezermake it easy to use scientific literate in any computational analysis such asmachine learning, natural language processing etc.  Availability: http://www.pubsqueezer.com

PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data