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.

该论文介绍了针对临床试验的自然语言推理（NLI4CT）任务的新方法。我们提出了 TLDR（利用 T5 生成的临床语言摘要进行 DeBERTa 报告分析），其结合了 T5 模型生成的前提摘要，以改进临床 NLI 任务中的蕴涵和矛盾分析。这种方法克服了窗口较小和前提较长带来的挑战，显著提高了宏观 F1 得分：比截断前提提高了 0.184。我们的全面实验评估，包括详细的错误分析和消融实验，证实了 TLDR 在预测一致性和忠实度方面对语义变化输入的优越性。

SemEval-2024 任务 2：T5 生成的 DeBERTa 报告分析的临床语言摘要

TLDR at SemEval-2024 Task 2: T5-generated clinical-Language summaries  for DeBERTa Report Analysis

Scientific extreme summarization (TLDR) aims to form ultra-short summaries of
scientific papers. Previous efforts on curating scientific TLDR datasets failed
to scale up due to the heavy human annotation and domain expertise required. In
this paper, we propose a simple yet effective approach to automatically
extracting TLDR summaries for scientific papers from their citation texts.
Based on the proposed approach, we create a new benchmark CiteSum without human
annotation, which is around 30 times larger than the previous human-curated
dataset SciTLDR. We conduct a comprehensive analysis of CiteSum, examining its
data characteristics and establishing strong baselines. We further demonstrate
the usefulness of CiteSum by adapting models pre-trained on CiteSum (named
CITES) to new tasks and domains with limited supervision. For scientific
extreme summarization, CITES outperforms most fully-supervised methods on
SciTLDR without any fine-tuning and obtains state-of-the-art results with only
128 examples. For news extreme summarization, CITES achieves significant gains
on XSum over its base model (not pre-trained on CiteSum), e.g., +7.2 ROUGE-1
zero-shot performance and state-of-the-art few-shot performance. For news
headline generation, CITES performs the best among unsupervised and zero-shot
methods on Gigaword. Our dataset and code can be found at
this https URL.

本文提出了一种从引文文本中自动提取科学论文 TLDR 摘要的简单有效方法，并基于此提出了一个新的基准数据集 CiteSum，该数据集比之前的人工筛选的数据集 SciTLDR 大约 30 倍，使用此数据集进行训练的模型在少量样例下即可取得明显提升的效果。