  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.
