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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Curriculum-guided Abstractive Summarization for Mental Health Online Posts
%A Sotudeh, Sajad
%A Goharian, Nazli
%A Deilamsalehy, Hanieh
%A Dernoncourt, Franck
%Y Lavelli, Alberto
%Y Holderness, Eben
%Y Jimeno Yepes, Antonio
%Y Minard, Anne-Lyse
%Y Pustejovsky, James
%Y Rinaldi, Fabio
%S Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F sotudeh-etal-2022-curriculum
%X 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.
%R 10.18653/v1/2022.louhi-1.17
%U https://aclanthology.org/2022.louhi-1.17
%U https://doi.org/10.18653/v1/2022.louhi-1.17
%P 148-153

Markdown (Informal)

[Curriculum-guided Abstractive Summarization for Mental Health Online Posts](https://aclanthology.org/2022.louhi-1.17) (Sotudeh et al., Louhi 2022)

• Curriculum-guided Abstractive Summarization for Mental Health Online Posts (Sotudeh et al., Louhi 2022)

ACL

• Sajad Sotudeh, Nazli Goharian, Hanieh Deilamsalehy, and Franck Dernoncourt. 2022. Curriculum-guided Abstractive Summarization for Mental Health Online Posts. In Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI), pages 148–153, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.