diff --git a/README.md b/README.md
index f099551f5a06de809f9c7e66a9923eb02e8e6761..a6a42e7b8f2a799f3aa7524d8e8c75537abb0ea7 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,40 @@ Eventually, it aims to find an unsupervised video summarization method, in order
 * [Conda cheat sheet](https://gitlab.uni-hannover.de/hussainkanafani/unsupervised-video-summarization/-/wikis/Conda-cheat-sheet)
 
 
+# Datasets
+Structured h5 files with the video features and annotations of the SumMe and TVSum datasets are available within the "data" folder. The GoogleNet features of the video frames were extracted by [Ke Zhang](https://github.com/kezhang-cs) and [Wei-Lun Chao] and the h5 files were obtained from [Kaiyang Zhou](https://github.com/KaiyangZhou/pytorch-vsumm-reinforce). 
+
+These files have the following structure:
+```
+/key
+    /features                 2D-array with shape (n_steps, feature-dimension)
+    /gtscore                  1D-array with shape (n_steps), stores ground truth improtance score (used for training, e.g. regression loss)
+    /user_summary             2D-array with shape (num_users, n_frames), each row is a binary vector (used for test)
+    /change_points            2D-array with shape (num_segments, 2), each row stores indices of a segment
+    /n_frame_per_seg          1D-array with shape (num_segments), indicates number of frames in each segment
+    /n_frames                 number of frames in original video
+    /picks                    positions of subsampled frames in original video
+    /n_steps                  number of subsampled frames
+    /gtsummary                1D-array with shape (n_steps), ground truth summary provided by user (used for training, e.g. maximum likelihood)
+    /video_name (optional)    original video name, only available for SumMe dataset
+```
+Original videos and annotations for each dataset are also available in the authors' project webpages:
+
+**TVSum dataset**: [https://github.com/yalesong/tvsum](https://github.com/yalesong/tvsum) 
+
+
+**SumMe dataset**: [https://gyglim.github.io/me/vsum/index.html#benchmark](https://gyglim.github.io/me/vsum/index.html#benchmark)
+
+
+
+### CSNet
+We used the implementation of [SUM-GAN](https://github.com/j-min/Adversarial_Video_Summary) method as a starting point to implement CSNet.
+
+#### How to train
+The implementation of CSNet is located under the directory csnet. Run main.py file with the configurations specified in configs.py to train the model.
+
+
+
 ### SUM-Ind
 Make splits
 ```bash
@@ -45,7 +79,7 @@ python main.py -d datasets/eccv16_dataset_summe_google_pool5.h5 -s datasets/summ
 ```
 
 
-### Citation
+### Citations
 ```
 @article{zhou2017reinforcevsumm, 
    title={Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward},