Information that human should read grows exponentially. To deal with this problem, computational linguistics and natural language processing communities have attempted to automate summarizing text. Since its start in 1950’s, automated text summarization has handled single-/multi-document summarization using extracting and abstracting techniques, and nowadays specialized its tasks to query-focused multi- document summarization. This paper gives the current state of automatic text summarization techniques focusing on robust, practical extraction- based methods, and describes evaluation methodologies and large-scale summarization evaluation conferences. Finally, future issues are discussed.