Domain-specific Guided Summarization for Mental Health Posts

• URL: http://arxiv.org/abs/2411.01485v1
• Date: Sun, 03 Nov 2024 08:57:41 GMT
• Title: Domain-specific Guided Summarization for Mental Health Posts
• Authors: Lu Qian, Yuqi Wang, Zimu Wang, Haiyang Zhang, Wei Wang, Ting Yu, Anh Nguyen,
• Abstract summary: We introduce a guided summarizer equipped with a dual-encoder and an adapted decoder. We present a post-editing correction model to rectify errors in the generated summary. Although the experiments are specifically designed for mental health posts, the methodology we've developed offers broad applicability.
• Score: 18.754472525614304
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In domain-specific contexts, particularly mental health, abstractive summarization requires advanced techniques adept at handling specialized content to generate domain-relevant and faithful summaries. In response to this, we introduce a guided summarizer equipped with a dual-encoder and an adapted decoder that utilizes novel domain-specific guidance signals, i.e., mental health terminologies and contextually rich sentences from the source document, to enhance its capacity to align closely with the content and context of guidance, thereby generating a domain-relevant summary. Additionally, we present a post-editing correction model to rectify errors in the generated summary, thus enhancing its consistency with the original content in detail. Evaluation on the MentSum dataset reveals that our model outperforms existing baseline models in terms of both ROUGE and FactCC scores. Although the experiments are specifically designed for mental health posts, the methodology we've developed offers broad applicability, highlighting its versatility and effectiveness in producing high-quality domain-specific summaries.

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