Replicating Research: Feature Vectors of Audio Samples
Here's my interpretation, and I'll summarize using Python's Librosa module:

Each frame is n_fft=512 samples (23.2 ms at sr=22050 Hz sampling rate), and hop_length=256 samples to get 50% overlap. Let y be the audio signal vector. When you run

mfcc_mat = librosa.feature.mfcc(y=y, sr=sr, n_fft=512, hop_length=256, n_mfcc=40)


you'll get a matrix with 40 rows ("coefficients") and $\approx$len(y)/hop_length columns ("time slices"). The authors only use the first 25 rows, and collapse the time dimension in 11 different ways (min, max, median, mean, etc.). Here's the (incomplete) code snippet that generates the 275-length feature vector:

metric_min = np.min( mfcc_mat[0:25,:], axis=1)
metric_max = np.max( mfcc_mat[0:25,:], axis=1)
metric_median = np.median( mfcc_mat[0:25,:], axis=1)
...
feature_vector = np.hstack((metric_min, metric_max, metric_median,...))


etc.

https://librosa.github.io/librosa/generated/librosa.feature.mfcc.html
https://librosa.github.io/librosa/generated/librosa.feature.melspectrogram.html#librosa.feature.melspectrogram