用专业术语引导生成更准确的心理健康内容摘要
Domain-specific Guided Summarization for Mental Health Posts
- 引入双编码器与改进解码器，结合专业术语和原文语境生成引导摘要
- 在MentSum数据集上，ROUGE和FactCC得分均优于现有模型
- 适合需要精准、可靠摘要的心理健康文本处理场景
在特定领域，尤其是心理健康领域，抽象摘要需要先进方法来处理专业内容，以生成相关且忠实的摘要。为此，我们提出一种带有双编码器和改进解码器的引导式摘要模型，利用新型领域特定引导信号——即心理健康术语和源文档中语义丰富的句子——增强模型对内容与上下文的对齐能力，从而生成领域相关的摘要。此外，我们还提出一个后编辑修正模型，用于纠正生成摘要中的错误，提升其与原文细节的一致性。在MentSum数据集上的评估表明，该模型在ROUGE和FactCC评分上均优于现有基线模型。尽管实验聚焦于心理健康帖子，但所开发的方法具有广泛适用性，展现出在生成高质量领域特定摘要方面的有效性与通用性。
原文摘要 · Abstract (English)
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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