  With an ever increasing size of text present on the Internet, automatic
summary generation remains an important problem for natural language
understanding. In this work we explore a novel full-fledged pipeline for text
summarization with an intermediate step of Abstract Meaning Representation
(AMR). The pipeline proposed by us first generates an AMR graph of an input
story, through which it extracts a summary graph and finally, generate summary
sentences from this summary graph. Our proposed method achieves
state-of-the-art results compared to the other text summarization routines
based on AMR. We also point out some significant problems in the existing
evaluation methods, which make them unsuitable for evaluating summary quality.
