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.
Abstract (translated)
在特定领域，特别是在心理健康领域，抽象概括需要高级技术来处理专业内容，以生成相关且忠实的摘要。为此，我们引入了一个配备了双编码器和适应性解码器的引导式摘要工具，该解码器利用新颖的领域专用指导信号（即心理健康的术语和来自源文档的语境丰富的句子），增强其与指南的内容和语境紧密对齐的能力，从而生成相关的领域摘要。此外，我们还提出了一种后编辑校正模型来修正生成摘要中的错误，从而使摘要在细节上更加一致于原始内容。在MentSum数据集上的评估显示，我们的模型在ROUGE和FactCC评分方面均优于现有的基线模型。虽然实验是专门针对心理健康帖子设计的，但我们开发的方法具有广泛的应用性，突显了其在生成高质量领域特定摘要方面的多样性和有效性。
URL
https://arxiv.org/abs/2411.01485
