We say a system is abstractive if it tries to find the optimal sequence from this set Y, under a scoring function s : X × Y → R. Contrast this to a fully extractive sentence summary 3 which transfers words from the input: or to the related problem of sentence compression that concentrates on deleting words from the input: While abstractive summarization poses a more difficult generation challenge, the lack of hard constraints gives the system more freedom in generation and allows it to fit with a wider range of training data.
in this work we focus on factored scoring functions, s, that take into account a fixed window of previous words: where we define y c y [i−C+1,...,i] for a window of size C. consider the conditional logprobability of a summary given the input, s(x, y) = log p(y|x; θ).