This book introduces automatic text summarization approaches with improved coherence, presenting the modeling of three categories of coherence in detail – shallow content-driven coherence, deep content-driven coherence, and cognitive model-driven coherence.  The computational modeling of such coherence, coupled with proposition-level extractive summarization, works successfully for narrative text. To model coherence of different kinds, novel techniques that are suitable for different genres of text, including newswires, social media messages, and fairy tales have been developed. The extensive experimental results on benchmark or self-compiled datasets have validated the efficacy and robustness of the techniques in various circumstances. Among its many contributions to summarization, this book shows that, contrary to common belief, coherence plays a pivotal role in automatic summarization, not an ancillary one. As one of the few large-scale studies of coherence in summarization, this book heralds a complete theory of coherence and more in-depth studies in coherence-targeted text summarization.