整合监督式抽取式和生成式语言模型用于自杀风险证据摘要
Integrating Supervised Extractive and Generative Language Models for Suicide Risk Evidence Summarization
AI总结：
本文提出整合监督式抽取式与生成式语言模型的方法，通过BERT评估句子级自杀风险与负面情绪，结合MentaLLaMa框架与专用词汇表生成摘要，在CLPsych 2024共享任务中取得高亮提取第一、摘要生成第十的成绩。
摘要：
我们提出一种整合监督式抽取式和生成式语言模型的方法，用于在 CLPsych 2024 共享任务中提供自杀风险的支持证据。我们的方法包含三个步骤。首先，我们构建一个基于 BERT 的模型，用于估计句子级别的自杀风险和负面情绪。接着，我们通过强调自杀风险和负面情绪的高概率，精确识别高自杀风险句子。最后，我们利用 MentaLLaMa 框架整合生成式摘要，并结合识别出的高自杀风险句子与专门的自杀风险词汇表提取摘要。我们的团队 SophiaADS 在高亮提取任务中获得第一名，在摘要生成任务中排名第十，两者分别基于召回率和一致性指标。
英文摘要：
We propose a method that integrates supervised extractive and generative language models for providing supporting evidence of suicide risk in the CLPsych 2024 shared task. Our approach comprises three steps. Initially, we construct a BERT-based model for estimating sentence-level suicide risk and negative sentiment. Next, we precisely identify high suicide risk sentences by emphasizing elevated probabilities of both suicide risk and negative sentiment. Finally, we integrate generative summaries using the MentaLLaMa framework and extractive summaries from identified high suicide risk sentences and a specialized dictionary of suicidal risk words. SophiaADS, our team, achieved 1st place for highlight extraction and ranked 10th for summary generation, both based on recall and consistency metrics, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
