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:
The dataset consists of 198,112 news articles.
Question: How many articles did they have?

Ex Output:
198,112

Ex Input:
Introduced by BIBREF16 , in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section. The corpus consists of all of the English Wikipedia paragraphs, except the abstracts. The released dataset has five predefined folds, and we use the first four as a training set (approximately 3M queries), and the remaining as a validation set (approximately 700k queries). The test set is the same one used to evaluate the submissions to TREC-CAR 2017 (approx. 1,800 queries).
Question: What is the TREC-CAR dataset?

Ex Output:
in this dataset, the input query is the concatenation of a Wikipedia article title with the title of one of its section. The relevant passages are the paragraphs within that section

Ex Input:
A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.

While the co-occurrence representation yields good results in classification scenarios, some important features are not considered in the model. For example, long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach BIBREF12.
Question: What is the reason that traditional co-occurrence networks fail in establishing links between similar words whenever they appear distant in the text?

Ex Output:
long-range syntactical links, though less frequent than adjacent syntactical relationships, might be disregarded from a simple word adjacency approach