  Sentence extraction based summarization methods has some limitations as it
doesn't go into the semantics of the document. Also, it lacks the capability of
sentence generation which is intuitive to humans. Here we present a novel
method to summarize text documents taking the process to semantic levels with
the use of WordNet and other resources, and using a technique for sentence
generation. We involve semantic role labeling to get the semantic
representation of text and use of segmentation to form clusters of the related
pieces of text. Picking out the centroids and sentence generation completes the
task. We evaluate our system against human composed summaries and also present
an evaluation done by humans to measure the quality attributes of our
summaries.
