ArXiv Preprint
Automatically generating short summaries from users' online mental health
posts could save counselors' reading time and reduce their fatigue so that they
can provide timely responses to those seeking help for improving their mental
state. Recent Transformers-based summarization models have presented a
promising approach to abstractive summarization. They go beyond sentence
selection and extractive strategies to deal with more complicated tasks such as
novel word generation and sentence paraphrasing. Nonetheless, these models have
a prominent shortcoming; their training strategy is not quite efficient, which
restricts the model's performance. In this paper, we include a curriculum
learning approach to reweigh the training samples, bringing about an efficient
learning procedure. We apply our model on extreme summarization dataset of
MentSum posts -- a dataset of mental health related posts from Reddit social
media. Compared to the state-of-the-art model, our proposed method makes
substantial gains in terms of Rouge and Bertscore evaluation metrics, yielding
3.5% (Rouge-1), 10.4% (Rouge-2), and 4.7% (Rouge-L), 1.5% (Bertscore) relative
improvements.
Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy, Franck Dernoncourt
2023-02-02
