In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
However, the coherence of the topics produced by LDA is poorer than expected.

To address this lack of coherence, we applied non-negative matrix factorization (NMF).

Ex Output:
How are prominent topics idenified in Dabiq and Rumiyah?


Ex Input:
As we mention in the Section 2, CAEVO is the current state-of-the-art system for feature-based temporal event relation extraction BIBREF10 . It's widely used as the baseline for evaluating TB-Dense data. We adopt it as our baseline for evaluating CaTeRS and RED datasets.

Ex Output:
What were the traditional linguistic feature-based models?


Ex Input:
Experiments ::: Evaluation Metrics ::: Setup of Automatic Evaluation ::: Language Fluency
We fine-tuned the GPT-2 medium model BIBREF51 on our collected headlines and then used it to measure the perplexity (PPL) on the generated outputs.

Ex Output:

How is fluency automatically evaluated?