  Online encyclopediae like Wikipedia contain large amounts of text that need
frequent corrections and updates. The new information may contradict existing
content in encyclopediae. In this paper, we focus on rewriting such dynamically
changing articles. This is a challenging constrained generation task, as the
output must be consistent with the new information and fit into the rest of the
existing document. To this end, we propose a two-step solution: (1) We identify
and remove the contradicting components in a target text for a given claim,
using a neutralizing stance model; (2) We expand the remaining text to be
consistent with the given claim, using a novel two-encoder sequence-to-sequence
model with copy attention. Applied to a Wikipedia fact update dataset, our
method successfully generates updated sentences for new claims, achieving the
highest SARI score. Furthermore, we demonstrate that generating synthetic data
through such rewritten sentences can successfully augment the FEVER
fact-checking training dataset, leading to a relative error reduction of 13%.
