Extractive vs. Abstractive Facts
It is of note that there are two broad strategies one might take when extracting facts, namely extractive or abstractive.
The dataset that we provide participants is already divided into 'stream items', e.g. sentences from a news article, or the
text of a tweet.
Extractive approaches may simply consider each of these stream items as candidate facts, and as such treat the
broader CrisisFACTS task as a stream item filtering and scoring problem, where the goal is to cluster the stream items based
on the information they contain, select an exemplar from each cluster, and then score each exemplar/cluster by its percieved
Abstractive approaches on the other hand aim to generate new custom fact text, using the stream items as input.
An abtractive approach might still cluster the stream items based on the information contained, but instead of selecting an
exemplar from the cluster, it would generate a new piece of fact text using the cluster as input, theoretically producing a
more concise and targeted fact than extractive approaches.