Curriculum-guided Abstractive Summarization for Mental Health Online Posts
This work addresses the need for efficient summarization to save counselors' time and reduce fatigue in mental health support, though it is incremental as it builds on existing models with a new training strategy.
The paper tackles the problem of inefficient training in Transformers-based abstractive summarization models for mental health online posts by incorporating a curriculum learning approach to reweigh training samples, resulting in substantial gains such as 10.4% relative improvement in Rouge-2 and 1.5% in Bertscore compared to the state-of-the-art.
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.