Question: How is sensationalism scorer trained?

Evidence:
raw_evidence: To evaluate the sensationalism intensity score $\alpha _{\text{sen}}$ of a headline, we collect a sensationalism dataset and then train a sensationalism scorer. For the sensationalism dataset collection, we choose headlines with many comments from popular online websites as positive samples. For the negative samples, we propose to use the generated headlines from a sentence summarization model. Intuitively, the summarization model, which is trained to preserve the semantic meaning, will lose the sensationalization ability and thus the generated negative samples will be less sensational than the original one, similar to the obfuscation of style after back-translation BIBREF4. For example, an original headline like UTF8gbsn“一趟挣10万？铁总增开申通、顺丰专列" (One trip to earn 100 thousand? China Railway opens new Shentong and Shunfeng special lines) will become UTF8gbsn“中铁总将增开京广两列快递专列" (China Railway opens two special lines for express) from the baseline model, which loses the sensational phrases of UTF8gbsn“一趟挣10万？" (One trip to earn 100 thousand?) . We then train the sensationalism scorer by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$. Firstly, 1-D convolution is used to extract word features from the input embeddings of a headline. This is followed by a ReLU activation layer and a max-pooling layer along the time dimension. All features from different channels are concatenated together and projected to the sensationalism score by adding another fully connected layer with sigmoid activation. Binary cross entropy is used to compute the loss $L_{\text{sen}}$.
raw_evidence: To evaluate the sensationalism intensity score $\alpha _{\text{sen}}$ of a headline, we collect a sensationalism dataset and then train a sensationalism scorer. For the sensationalism dataset collection, we choose headlines with many comments from popular online websites as positive samples. For the negative samples, we propose to use the generated headlines from a sentence summarization model. Intuitively, the summarization model, which is trained to preserve the semantic meaning, will lose the sensationalization ability and thus the generated negative samples will be less sensational than the original one, similar to the obfuscation of style after back-translation BIBREF4. For example, an original headline like UTF8gbsn“一趟挣10万？铁总增开申通、顺丰专列" (One trip to earn 100 thousand? China Railway opens new Shentong and Shunfeng special lines) will become UTF8gbsn“中铁总将增开京广两列快递专列" (China Railway opens two special lines for express) from the baseline model, which loses the sensational phrases of UTF8gbsn“一趟挣10万？" (One trip to earn 100 thousand?) . We then train the sensationalism scorer by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$. Firstly, 1-D convolution is used to extract word features from the input embeddings of a headline. This is followed by a ReLU activation layer and a max-pooling layer along the time dimension. All features from different channels are concatenated together and projected to the sensationalism score by adding another fully connected layer with sigmoid activation. Binary cross entropy is used to compute the loss $L_{\text{sen}}$.

Answers:
by classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss $L_{\text{sen}}$
classifying sensational and non-sensational headlines using a one-layer CNN with a binary cross entropy loss