Integrating Supervised Extractive and Generative Language Models for Suicide Risk Evidence Summarization

arXiv:2403.15478v1 Announce Type: new
Abstract: 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 …

abstract arxiv bert construct cs.cl evidence generative language language models negative risk suicide summarization type