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.

Ex Input:
Table TABREF23 shows the Pearson, Spearman and Kendall correlation of Rouge and Sera, with pyramid scores. Interestingly, we observe that many variants of Rouge scores do not have high correlations with human pyramid scores. The lowest F-score correlations are for Rouge-1 and Rouge-L (with INLINEFORM0 =0.454). Weak correlation of Rouge-1 shows that matching unigrams between the candidate summary and gold summaries is not accurate in quantifying the quality of the summary.
Question: What different correlations result when using different variants of ROUGE scores?

Ex Output:
we observe that many variants of Rouge scores do not have high correlations with human pyramid scores

Ex Input:
We used a dataset provided by Shortir, an Indonesian news aggregator and summarizer company. The dataset contains roughly 20K news articles. Each article has the title, category, source (e.g., CNN Indonesia, Kumparan), URL to the original article, and an abstractive summary which was created manually by a total of 2 native speakers of Indonesian
Question: Did they use a crowdsourcing platform for the summaries?

Ex Output:
No

Ex Input:
Last, we evaluate our approaches in 9 commonly used text classification datasets. We evaluate our methods on several commonly used datasets whose themes range from sentiment, web-page, science to medical and healthcare.
Question: What NLP tasks do they consider?

Ex Output:
text classification for themes including sentiment, web-page, science, medical and healthcare