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: For task 1, we use F1-score as evaluation metric. We use manual evaluation for task 2. There are five evaluation metrics for task 2 as following.

Task completion ratio: The number of completed tasks divided by the number of total tasks.

User satisfaction degree: There are five scores -2, -1, 0, 1, 2, which denote very dissatisfied, dissatisfied, neutral, satisfied and very satisfied, respectively.

Response fluency: There are three scores -1, 0, 1, which indicate nonfluency, neutral, fluency.

Number of dialogue turns: The number of utterances in a task-completed dialogue.

Guidance ability for out of scope input: There are two scores 0, 1, which represent able to guide or unable to guide.
Question: What metrics are used in the evaluation?
A:
For task 1, we use F1-score Task completion ratio User satisfaction degree Response fluency Number of dialogue turns Guidance ability for out of scope input