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.

我们使用监督的抽取和生成式语言模型集成的方法来提供 2024 年 CLPsych 共享任务中对自杀风险的支持证据。我们的方法包括三个步骤：首先，构建一个基于 BERT 的模型来估计句子级的自杀风险和负面情绪；接下来，通过强调自杀风险和负面情绪的高概率，精确识别出高自杀风险的句子；最后，我们使用 MentaLLaMa 框架的生成式摘要和从高自杀风险句子和专门的自杀风险词典中的抽取式摘要进行集成。我们的团队 SophiaADS 在突出摘要方面获得第一名，并在摘要生成方面在基于回收率和一致性度量标准的排名中排名第 10。