  Story Ending Generation (SEG) is a challenging task in natural language
generation. Recently, methods based on Pre-trained Language Models (PLM) have
achieved great prosperity, which can produce fluent and coherent story endings.
However, the pre-training objective of PLM-based methods is unable to model the
consistency between story context and ending. The goal of this paper is to
adopt contrastive learning to generate endings more consistent with story
context, while there are two main challenges in contrastive learning of SEG.
First is the negative sampling of wrong endings inconsistent with story
contexts. The second challenge is the adaptation of contrastive learning for
SEG. To address these two issues, we propose a novel Contrastive Learning
framework for Story Ending Generation (CLSEG), which has two steps:
multi-aspect sampling and story-specific contrastive learning. Particularly,
for the first issue, we utilize novel multi-aspect sampling mechanisms to
obtain wrong endings considering the consistency of order, causality, and
sentiment. To solve the second issue, we well-design a story-specific
contrastive training strategy that is adapted for SEG. Experiments show that
CLSEG outperforms baselines and can produce story endings with stronger
consistency and rationality.
