  This paper introduces novel methodologies for the Natural Language Inference
for Clinical Trials (NLI4CT) task. We present TLDR (T5-generated
clinical-Language summaries for DeBERTa Report Analysis) which incorporates
T5-model generated premise summaries for improved entailment and contradiction
analysis in clinical NLI tasks. This approach overcomes the challenges posed by
small context windows and lengthy premises, leading to a substantial
improvement in Macro F1 scores: a 0.184 increase over truncated premises. Our
comprehensive experimental evaluation, including detailed error analysis and
ablations, confirms the superiority of TLDR in achieving consistency and
faithfulness in predictions against semantically altered inputs.
