LongStory: Coherent, Complete and Length Controlled Long story
  Generation
Kyeongman Park Nakyeong Yang Kyomin Jung
A human author can write any length of story without losing coherence. Also,
they always bring the story to a proper ending, an ability that current
language models lack. In this work, we present the LongStory for coherent,
complete, and length-controlled long story generation. LongStory introduces two
novel methodologies: (1) the long and short-term contexts weight calibrator
(CWC) and (2) long story structural positions (LSP). The CWC adjusts weights
for long-term context Memory and short-term context Cheating, acknowledging
their distinct roles. The LSP employs discourse tokens to convey the structural
positions of a long story. Trained on three datasets with varied average story
lengths, LongStory outperforms other baselines, including the strong story
generator Plotmachine, in coherence, completeness, relevance, and
repetitiveness. We also perform zero-shot tests on each dataset to assess the
model's ability to predict outcomes beyond its training data and validate our
methodology by comparing its performance with variants of our model.