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.
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Question: We compared several summarization methods which can be categorized into three groups: unsupervised, non-neural supervised, and neural supervised methods.  As a baseline, we used Lead-N which selects INLINEFORM0 leading sentences as the summary. For all methods, we extracted 3 sentences as the summary since it is the median number of sentences in the gold summaries that we found in our exploratory analysis. Lead-3 baseline performs really well and outperforms almost all the other models, which is not surprising and even consistent with other work that for news summarization, Lead-N baseline is surprisingly hard to beat.

Answer: What was the best performing baseline?


Question: The current state-of-the-art approach BIBREF14 , BIBREF15 uses maximum entropy and CRF models with a combination of language model and hand-crafted features to predict if each character in the hashtag is the beginning of a new word.

Answer: What current state of the art method was used for comparison?


Question: Since the ground-truth reference captions in ShapeWorld are randomly sampled, we take the sampled captions accompanying the test images as a proxy for optimal caption diversity, and compare it with the empirical output diversity of the evaluated model on these test images. Practically, we look at language constructions used and compute the corresponding diversity score as the ratio of observed number versus optimal number

Answer:
How is diversity measured?