You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
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.

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?
Solution: No
Why? Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

New input: The first metric we report is the reaction type. The second metric we report is reaction speed. A study by Jin et al. jin2013epidemiological found that trusted news stories spread faster than misinformation or rumor; Zeng et al. zeng2016rumors found that tweets which deny rumors had shorter delays than tweets of support. Our second goal is to determine if these trends are maintained for various types of news sources on Twitter and Reddit. To examine whether users react to content from trusted sources differently than from deceptive sources, we measure the reaction delay, which we define as the time elapsed between the moment the link or content was posted/tweeted and the moment that the reaction comment or tweet occurred. We report the cumulative distribution functions (CDFs) for each source type and use Mann Whitney U (MWU) tests to compare whether users respond with a given reaction type with significantly different delays to news sources of different levels of credibility.
Question: How is speed measured?
Solution:
time elapsed between the moment the link or content was posted/tweeted and the moment that the reaction comment or tweet occurred