  Despite recent advancements, existing story generation systems continue to
encounter difficulties in effectively incorporating contextual and event
features, which greatly influence the quality of generated narratives. To
tackle these challenges, we introduce a novel neural generation model, EtriCA,
that enhances the relevance and coherence of generated stories by employing a
cross-attention mechanism to map context features onto event sequences through
residual mapping. This feature capturing mechanism enables our model to exploit
logical relationships between events more effectively during the story
generation process. To further enhance our proposed model, we employ a
post-training framework for knowledge enhancement (KeEtriCA) on a large-scale
book corpus. This allows EtriCA to adapt to a wider range of data samples. This
results in approximately 5\% improvement in automatic metrics and over 10\%
improvement in human evaluation. We conduct extensive experiments, including
comparisons with state-of-the-art (SOTA) baseline models, to evaluate the
performance of our framework on story generation. The experimental results,
encompassing both automated metrics and human assessments, demonstrate the
superiority of our model over existing state-of-the-art baselines. These
results underscore the effectiveness of our model in leveraging context and
event features to improve the quality of generated narratives.
