We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a targeted domain such as “surfing,” our system mines the YouTube database to find pairs of raw and their corresponding edited videos. Leveraging the assumption that an edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to mitigate the issues caused by the noise. We efficiently learn the latent model on a large number of videos (about 870 min in total) using a novel EM-like procedure. Our latent ranking model outperforms its classification counterpart and is fairly competitive compared with a fully supervised ranking system that requires labels from Amazon Mechanical Turk. We further show that a state-of-the-art audio feature mel-frequency cepstral coefficients is inferior to a state-of-the-art visual feature. By combining both audio-visual features, we obtain the best performance in dog activity, surfing, skating, and viral video domains. Finally, we show that impressive highlights can be detected without additional human supervision for seven domains (i.e., skating, surfing, skiing, gymnastics, parkour, dog activity, and viral video) in unconstrained personal videos.