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In a world of proliferating data, the abil- ity to rapidly summarize text is grow- ing in importance. Automatic summariza- tion of text can be thought of as a se- quence to sequence problem. Another area of natural language processing that solves a sequence to sequence problem is ma- chine translation, which is rapidly evolv- ing due to the development of attention- based encoder-decoder networks. This work applies these modern techniques to abstractive summarization. We perform analysis on various attention mechanisms for summarization with the goal of devel- oping an approach and architecture aimed at improving the state of the art. In par- ticular, we modify and optimize a trans- lation model with self-attention for gener- ating abstractive sentence summaries. The effectiveness of this base model along with attention variants is compared and ana- lyzed in the context of standardized eval- uation sets and test metrics. However, we show that these metrics are limited in their ability to effectively score abstractive summaries, and propose a new approach based on the intuition that an abstractive model requires an abstractive evaluation.