Part 1. Definition
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.
Part 2. 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?
Answer: No
Explanation: Based on the context, the dataset is constructed from a famous Chinese social media microblogging service Weibo.
Part 3. Exercise
We have evaluated our models considering the F1 Score, which is the harmonic mean of precision and recall. We have run ten times the experiment for each model and considered the average F1 Score. The results are mentioned in Table TABREF11. Considering F1 Macro the models that include the multi-attention mechanism outperform the others and particularly the one with the Projected Layer has the highest performance. In three out of four pairs of models, the ones with the Projected Layer achieved better performance, so in most cases the addition of the Projected Layer had a significant enhancement.

Question: Which variation provides the best results on this dataset?
Answer:
the model with multi-attention mechanism and a projected layer