We introduce Textstar, a graph-based summarization and keyphrase extraction system that builds a document graph using only lemmatization and POS tagging. The document graph aggregates connections between lemma and sentence identifier nodes. Consecutive lemmas in each sentence, as well as consecutive sentences themselves, are connected in rings to form a ring of rings representing the document. We iteratively apply a centrality algorithm of our choice to the document graph and trim the lowest ranked nodes at each step. After the desired number of remaining sentences and lemmas is reached, we extract the sentences as the summary, and the remaining lemmas are aggregated into keyphrases using their context. Our algorithm is efficient enough to one-shot process large document graphs without any training, and empirical evaluation on several benchmarks indicates that our performance is higher than most other graph based algorithms.