TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report Analysis
<|reference_start|>TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries for DeBERTa Report Analysis: 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.<|reference_end|>
@article{das2024tldr,
    title={TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries
  for DeBERTa Report Analysis},
    author={Spandan Das, Vinay Samuel, and Shahriar Noroozizadeh},
    journal={In Proceedings of the 18th International Workshop on Semantic
  Evaluation (SemEval-2024), pages 507-516, Mexico City, Mexico. Association
  for Computational Linguistics},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.09136},
    primaryClass={cs.CL cs.AI cs.LG}
}