Guided Abstractive Summarization of Mental Health Posts Using Domain-Specific Knowledge and Post-Editing Correction

Core Concepts

This research introduces a novel guided summarization model, enhanced with domain-specific knowledge and a post-editing correction mechanism, to generate more relevant and faithful summaries of mental health posts.

Abstract

• Bibliographic Information: Qian, L., Wang, Y., Wang, Z., Zhang, H., Wang, W., Yu, T., & Nguyen, A. (2024). Domain-specific Guided Summarization for Mental Health Posts. arXiv preprint arXiv:2411.01485.
• Research Objective: This paper addresses the challenge of generating accurate and relevant summaries of mental health posts, aiming to improve the domain specificity and factual consistency of abstractive summarization models.
• Methodology: The researchers propose a two-stage approach:
  1. Guided Summarizer: A dual-encoder architecture leverages domain-specific guidance signals, including mental health terminologies and contextually rich sentences from the source post, to guide the summarization process.
  2. Post-editing Corrector: A BART-based corrector identifies and rectifies potential inconsistencies in the generated summary, ensuring closer alignment with the original content.
• Key Findings:
  • The proposed models, GSUM-TERM (using terminologies as guidance) and GSUM-SENT (using context-rich sentences as guidance), outperform baseline models on the MENTSUM dataset in terms of both ROUGE and FactCC scores.
  • GSUM-SENT, leveraging contextually rich sentences, achieves the highest performance, demonstrating the importance of context in generating accurate and relevant summaries.
  • The post-editing corrector effectively improves the factual consistency of the generated summaries, as evidenced by increased FactCC scores.
• Main Conclusions:
  • Incorporating domain-specific knowledge, particularly through context-rich sentences, significantly enhances the relevance and accuracy of mental health post summaries.
  • Post-editing correction plays a crucial role in ensuring the faithfulness of the generated summaries to the original content.
• Significance: This research contributes to the field of domain-specific abstractive summarization by introducing a novel approach that combines guided summarization with post-editing correction, demonstrating its effectiveness in the mental health domain.
• Limitations and Future Research:
  • The corrector's ability to address complex inaccuracies is limited by the scope of its training data. Future research could explore more diverse training datasets to enhance its correction capabilities.
  • The study focuses on the mental health domain. Further investigation is needed to assess the generalizability of this approach to other domains.

Stats

The MENTSUM dataset comprises over 24k post-TL;DR pairs, divided into 21,695 training, 1,209 validation, and 1,215 test instances.
On average, each post contains 327.5 words or 16.9 sentences, while TL;DR consists of 43.5 words or 2.6 sentences.
GSUM-TERM achieves a 1.5% higher FactCC score than BART and a 1.6% higher score than GSUM.
GSUM-SENT achieves a 2.7% higher FactCC score compared to BART and a 2.8% improvement over GSUM.
Only 10.3% of the summaries generated by GSUM-SENT undergo revisions by the corrector.
92.8% of the corrected summaries incorporate three or fewer new tokens, despite the summary averaging 53.27 tokens in length.

Quotes

"Mental health is a critical area that profoundly affects both individuals and society, demanding effective and accurate communication for support."
"The summary enables quicker review and response by professional counselors, thus enhancing support for individuals dealing with mental health issues and demonstrating significant social impact."
"This design is specifically tailored to enhance the summarization process within mental health contexts, guiding the generation of a summary that is both terminologically precise and richly informed by the underlying domain-specific information contained within the original text."

Key Insights Distilled From

Domain-specific Guided Summarization for Mental Health Posts

by Lu Qian, Yuq... at arxiv.org 11-05-2024

https://arxiv.org/pdf/2411.01485.pdf