  The prevalence of half-truths, which are statements containing some truth but
that are ultimately deceptive, has risen with the increasing use of the
internet. To help combat this problem, we have created a comprehensive pipeline
consisting of a half-truth detection model and a claim editing model. Our
approach utilizes the T5 model for controlled claim editing; "controlled" here
means precise adjustments to select parts of a claim. Our methodology achieves
an average BLEU score of 0.88 (on a scale of 0-1) and a disinfo-debunk score of
85% on edited claims. Significantly, our T5-based approach outperforms other
Language Models such as GPT2, RoBERTa, PEGASUS, and Tailor, with average
improvements of 82%, 57%, 42%, and 23% in disinfo-debunk scores, respectively.
By extending the LIAR PLUS dataset, we achieve an F1 score of 82% for the
half-truth detection model, setting a new benchmark in the field. While
previous attempts have been made at half-truth detection, our approach is, to
the best of our knowledge, the first to attempt to debunk half-truths.
