We live in an age of access to more information than ever before. This can be a 
double-edged sword. Increased access to information allows for more informed and 
empowered researchers, while information overload becomes an increasingly 
serious risk. Thus, there is a need for intelligent information retrieval 
systems that can summarize relevant and reliable textual sources to satisfy a 
user's query. Question answering is a specialized type of information retrieval 
with the aim of returning precise short answers to queries posed as natural 
language questions. We present a review and comparison of three biomedical 
question answering systems: askHERMES (http://www.askhermes.org/), EAGLi 
(http://eagl.unige.ch/EAGLi/), and HONQA 
(http://services.hon.ch/cgi-bin/QA10/qa.pl).