  New software and updates are downloaded by end users every day. Each
dowloaded software has associated with it an End Users License Agreements
(EULA), but this is rarely read. An EULA includes information to avoid legal
repercussions. However,this proposes a host of potential problems such as
spyware or producing an unwanted affect in the target system. End users do not
read these EULA's because of length of the document and users find it extremely
difficult to understand. Text summarization is one of the relevant solution to
these kind of problems. This require a solution which can summarize the EULA
and classify the EULA as "Benign" or "Malicious". We propose a solution in
which we have summarize the EULA and classify the EULA as "Benign" or
"Malicious". We extract EULA text of different sofware's then we classify the
text using eight different supervised classifiers. we use ensemble learning to
classify the EULA as benign or malicious using five different text
summarization methods. An accuracy of $95.8$\% shows the effectiveness of the
presented approach.
