Teacher: 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.
Teacher: Now, understand the problem? If you are still confused, see the following example:
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
Reason: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.

Now, solve this instance: The most intuitive way to represent syntactic information is to use individual dependency relations directly, like dependency head and dependency relation label, denoted as Dep and Rel for short. In order to preserve the structural information of dependency trees as much as possible, we take the syntactic path between candidate arguments and predicates in dependency trees as linguistic knowledge. Referring to BIBREF9, we use the Tree-based Position Feature (TPF) as Dependency Path (DepPath) and use the Shortest Dependency Path (SDP) as Relation Path (RelPath).
Question: What different approaches of encoding syntactic information authors present?
Student:
dependency head and dependency relation label, denoted as Dep and Rel for short Tree-based Position Feature (TPF) as Dependency Path (DepPath) Shortest Dependency Path (SDP) as Relation Path (RelPath)