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.

我们提出了 LongStory，一种用于连贯、完整且受长度控制的长篇故事生成的方法。它引入了两种新方法：长期和短期上下文权重校准器（CWC）和长篇故事结构位置（LSP）。在多个数据集上训练，LongStory 在连贯性、完整性、相关性和重复性方面优于其他基线，包括强大的故事生成器 Plotmachine。我们还对每个数据集进行了零样本测试，评估该模型在超出其训练数据范围的情况下预测结果的能力，并通过与我们模型的变体进行比较来验证我们的方法。

LongStory: 连贯、完整且长度可控的长篇故事生成

LongStory: Coherent, Complete and Length Controlled Long story  Generation

Existing Natural Language Generation (NLG) systems are weak AI systems and
exhibit limited capabilities when language generation tasks demand higher
levels of creativity, originality and brevity. Effective solutions or, at least
evaluations of modern NLG paradigms for such creative tasks have been elusive,
unfortunately. This paper introduces and addresses the task of coherent story
generation from independent descriptions, describing a scene or an event.
Towards this, we explore along two popular text-generation paradigms -- (1)
Statistical Machine Translation (SMT), posing story generation as a translation
problem and (2) Deep Learning, posing story generation as a sequence to
sequence learning problem. In SMT, we chose two popular methods such as phrase
based SMT (PB-SMT) and syntax based SMT (SYNTAX-SMT) to `translate' the
incoherent input text into stories. We then implement a deep recurrent neural
network (RNN) architecture that encodes sequence of variable length input
descriptions to corresponding latent representations and decodes them to
produce well formed comprehensive story like summaries. The efficacy of the
suggested approaches is demonstrated on a publicly available dataset with the
help of popular machine translation and summarization evaluation metrics.

本论文提出并解决了通过两种文本生成范例 —— 统计机器翻译和深度学习，生成连贯故事的任务。使用两种 SMT 方法和一个深度 RNN 架构，将可变长度的输入描述编码为对应的潜在表示，并解码以生成形式完备的故事总结。在一个公开数据集上，利用常见的机器翻译和总结评估方法证明了所建议方法的有效性。