Hallucination in text summarization refers to the phenomenon where the model
generates information that is not supported by the input source document.
Hallucination poses significant obstacles to the accuracy and reliability of
the generated summaries. In this paper, we aim to reduce hallucinated outputs
or hallucinations in summaries of long-form text documents. We have used the
PubMed dataset, which contains long scientific research documents and their
abstracts. We have incorporated the techniques of data filtering and joint
entity and summary generation (JAENS) in the fine-tuning of the Longformer
Encoder-Decoder (LED) model to minimize hallucinations and thereby improve the
quality of the generated summary. We have used the following metrics to measure
factual consistency at the entity level: precision-source, and F1-target. Our
experiments show that the fine-tuned LED model performs well in generating the
paper abstract. Data filtering techniques based on some preprocessing steps
reduce entity-level hallucinations in the generated summaries in terms of some
of the factual consistency metrics.

减少幻觉的输出或概括长文本文档摘要的幻觉是本论文的目标。通过在 Longformer Encoder-Decoder (LED) 模型的微调中结合数据过滤和联合实体与摘要生成（JAENS）技术，我们使用 PubMed 数据集来改善产生的摘要质量，并测量了实体级别的事实一致性。实验结果显示，经过微调的 LED 模型在生成论文摘要方面表现良好，基于一些预处理步骤的数据过滤技术能够减少生成摘要中的实体级别幻觉，从而提高了事实一致性指标。