Integrating Supervised Extractive and Generative Language Models for
  Suicide Risk Evidence Summarization
Rika Tanaka Yusuke Fukazawa
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.