Domain-specific Guided Summarization for Mental Health Posts

TL;DR

This paper presents a novel guided summarization model tailored for mental health posts, utilizing domain-specific signals and post-editing to improve relevance, accuracy, and faithfulness of summaries.

Contribution

Introduces a dual-encoder guided summarizer with domain-specific guidance signals and a post-editing correction model for mental health content.

Findings

Outperforms baseline models on ROUGE and FactCC scores

Effective in generating domain-relevant and faithful summaries

Method applicable to other specialized domains

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.