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Text coherence is a fundamental problem in natural language generation andunderstanding. Organizing sentences into an order that maximizes coherence isknown as sentence ordering. This paper is proposing a new approach based on thegraph neural network approach to encode a set of sentences and learn orderingsof short stories. We propose a new method for constructing sentence-entitygraphs of short stories to create the edges between sentences and reduce noisein our graph by replacing the pronouns with their referring entities. Weimprove the sentence ordering by introducing an aggregation method based onmajority voting of state-of-the-art methods and our proposed one. Our approachemploys a BERT-based model to learn semantic representations of the sentences.The results demonstrate that the proposed method significantly outperformsexisting baselines on a corpus of short stories with a new state-of-the-artperformance in terms of Perfect Match Ratio (PMR) and Kendall's Tau (Tau)metrics. More precisely, our method increases PMR and Tau criteria by more than5% and 4.3%, respectively. These outcomes highlight the benefit of forming theedges between sentences based on their cosine similarity. We also observe thatreplacing pronouns with their referring entities effectively encodes sentencesin sentence-entity graphs.

Pruned Graph Neural Network for Short Story Ordering