of each word into a Convolutional Neural Network (CNN) with max-pooling [@kim2014convolutional] to obtain representations $c = (c_1,...,c_l)$ and $c_n\in R^{n}$ for each word in the training corpus, $n$ is the character composition dimension. Finally, each word is represented as a concatenation of word-level embedding and character-composition vector $e_i = [w_i;c_i]$. A matrices $e^s\in R^{k*(m+n)}$ can be used to represent a news after the embedding layer, where $k$ is the length of the news.

### News Level Bi-LSTM and Self-Attention Layer

***Bi-LSTM Encoding***: After the embedding layer, we fed the words and their context in the news title into a Bi-LSTM based sentence encoder to perform distributional representation. Bidirectional LSTM (Bi-LSTM) is a variant of LSTM which shows better result then uni-direction LSTM in recent NLP tasks as they can understand context better. A bidirectional LSTM runs a forward and backward LSTM on a sequence starting from the left and the right end, respectively [@chen2017recurrent]. In this case, Bi-LSTM can not only preserve information from the past, but also catch the information from the future. We obtain the hidden vectors($\overrightarrow{h_i}$ and $\overleftarrow{h_i}$ shown in Equation (8) and (9)) from the sentence encoders and concatenate them to $H^t=[\overrightarrow{h_1};\overleftarrow{h_1},....,\overrightarrow{h_m};\overleftarrow{h_m}]$, $H_i^t\in R^{2u}$ represents 