Domain-specific Guided Summarization for Mental Health Posts
This work addresses the challenge of producing faithful summaries for mental health content, which is an incremental improvement in domain-specific summarization.
The authors tackled the problem of generating domain-relevant summaries for mental health posts by introducing a guided summarizer with a dual-encoder and adapted decoder, which outperformed baseline models on the MentSum dataset in terms of ROUGE and FactCC scores.
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.