In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Example input: We evaluate the proposed approach on the Chinese social media text summarization task, based on the sequence-to-sequence model. Large-Scale Chinese Short Text Summarization Dataset (LCSTS) is constructed by BIBREF1 . The dataset consists of more than 2.4 million text-summary pairs in total, constructed from a famous Chinese social media microblogging service Weibo.  
 Question: Are results reported only for English data?
Example output: No
Example explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Q: Our first baseline is ROUGE-L BIBREF1 , since it is the most commonly used metric for compression tasks. We compare to the best n-gram-overlap metrics from toutanova2016dataset; We further compare to the negative LM cross-entropy, i.e., the log-probability which is only normalized by sentence length. Our next baseline is perplexity, which corresponds to the exponentiated cross-entropy: Due to its popularity, we also performed initial experiments with BLEU BIBREF17 .  
 Question: Is ROUGE their only baseline?
A:
No