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.

使用文本摘要和集成学习方法，我们提出了一种能够对终端用户许可协议（EULA）进行摘要和分类的解决方案，其中我们提取了不同软件的 EULA 文本，然后使用八种有监督的分类器对文本进行分类。该方法的准确率为 95.8% 表明了其有效性。