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.
One example is below.
Q: 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?
A: No
Rationale: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Q: Our presented QA-CTS task unifies the output format of the traditional CTS task and make the training data shareable, thus enriching the training data. All question-answer pairs are annotated and reviewed by four clinicians with three types of questions, namely tumor size, proximal resection margin and distal resection margin. Experimental results on real-world dataset demonstrate that our proposed model competes favorably with strong baseline models in all three specific tasks.
Question: What are the specific tasks being unified?
A:
three types of questions, namely tumor size, proximal resection margin and distal resection margin